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mixture discriminant analysis</title> <meta name="description" content="Search results for: Gaussian mixture discriminant analysis"> <meta name="keywords" content="Gaussian mixture discriminant analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science 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method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Gaussian mixture discriminant analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 9268</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Gaussian mixture discriminant analysis</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9268</span> Self Organizing Mixture Network in Mixture Discriminant Analysis: An Experimental Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nazif%20%C3%87al%C4%B1%C5%9F">Nazif Çalış</a>, <a href="https://publications.waset.org/search?q=Murat%20Eri%C5%9Fo%C4%9Flu"> Murat Erişoğlu</a>, <a href="https://publications.waset.org/search?q=Hamza%20Erol"> Hamza Erol</a>, <a href="https://publications.waset.org/search?q=Tayfun%20Servi"> Tayfun Servi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the recent works related with mixture discriminant analysis (MDA), expectation and maximization (EM) algorithm is used to estimate parameters of Gaussian mixtures. But, initial values of EM algorithm affect the final parameters- estimates. Also, when EM algorithm is applied two times, for the same data set, it can be give different results for the estimate of parameters and this affect the classification accuracy of MDA. Forthcoming this problem, we use Self Organizing Mixture Network (SOMN) algorithm to estimate parameters of Gaussians mixtures in MDA that SOMN is more robust when random the initial values of the parameters are used [5]. We show effectiveness of this method on popular simulated waveform datasets and real glass data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Self%20Organizing%20Mixture%20Network" title="Self Organizing Mixture Network">Self Organizing Mixture Network</a>, <a href="https://publications.waset.org/search?q=MixtureDiscriminant%20Analysis" title=" MixtureDiscriminant Analysis"> MixtureDiscriminant Analysis</a>, <a href="https://publications.waset.org/search?q=Waveform%20Datasets" title=" Waveform Datasets"> Waveform Datasets</a>, <a href="https://publications.waset.org/search?q=Glass%20Identification" title=" Glass Identification"> Glass Identification</a>, <a href="https://publications.waset.org/search?q=Mixture%20of%20Multivariate%20Normal%20Distributions" title="Mixture of Multivariate Normal Distributions">Mixture of Multivariate Normal Distributions</a> </p> <a href="https://publications.waset.org/5583/self-organizing-mixture-network-in-mixture-discriminant-analysis-an-experimental-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5583/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5583/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5583/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5583/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5583/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5583/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5583/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5583/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5583/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5583/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5583.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1517</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9267</span> Real-time Tracking in Image Sequences based-on Parameters Updating with Temporal and Spatial Neighborhoods Mixture Gaussian Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hu%20Haibo">Hu Haibo</a>, <a href="https://publications.waset.org/search?q=Zhao%20Hong"> Zhao Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaussian mixture background model is widely used in moving target detection of the image sequences. However, traditional Gaussian mixture background model usually considers the time continuity of the pixels, and establishes background through statistical distribution of pixels without taking into account the pixels- spatial similarity, which will cause noise, imperfection and other problems. This paper proposes a new Gaussian mixture modeling approach, which combines the color and gradient of the spatial information, and integrates the spatial information of the pixel sequences to establish Gaussian mixture background. The experimental results show that the movement background can be extracted accurately and efficiently, and the algorithm is more robust, and can work in real time in tracking applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20mixture%20model" title="Gaussian mixture model">Gaussian mixture model</a>, <a href="https://publications.waset.org/search?q=real-time%20tracking" title=" real-time tracking"> real-time tracking</a>, <a href="https://publications.waset.org/search?q=sequence%20image" title="sequence image">sequence image</a>, <a href="https://publications.waset.org/search?q=gradient." title=" gradient."> gradient.</a> </p> <a href="https://publications.waset.org/9832/real-time-tracking-in-image-sequences-based-on-parameters-updating-with-temporal-and-spatial-neighborhoods-mixture-gaussian-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9832/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9832/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9832/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9832/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9832/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9832/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9832/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9832/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9832/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9832/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9832.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1478</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9266</span> Spectral Mixture Model Applied to Cannabis Parcel Determination </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Levent%20Basayigit">Levent Basayigit</a>, <a href="https://publications.waset.org/search?q=Sinan%20Demir"> Sinan Demir</a>, <a href="https://publications.waset.org/search?q=Yusuf%20Ucar"> Yusuf Ucar</a>, <a href="https://publications.waset.org/search?q=Burhan%20Kara"> Burhan Kara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20mixture%20discriminant%20analysis" title="Gaussian mixture discriminant analysis">Gaussian mixture discriminant analysis</a>, <a href="https://publications.waset.org/search?q=spectral%20mixture%20model" title=" spectral mixture model"> spectral mixture model</a>, <a href="https://publications.waset.org/search?q=World%20View-2" title=" World View-2"> World View-2</a>, <a href="https://publications.waset.org/search?q=land%20parcels." title=" land parcels. "> land parcels. </a> </p> <a href="https://publications.waset.org/10009502/spectral-mixture-model-applied-to-cannabis-parcel-determination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10009502/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10009502/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10009502/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10009502/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10009502/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10009502/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10009502/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10009502/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10009502/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10009502/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10009502.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">800</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9265</span> Unsupervised Texture Classification and Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=V.P.Subramanyam%20Rallabandi">V.P.Subramanyam Rallabandi</a>, <a href="https://publications.waset.org/search?q=S.K.Sett"> S.K.Sett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. When applied to textures, the algorithm can learn basis functions for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised texture classification and segmentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20Mixture%20Model" title="Gaussian Mixture Model">Gaussian Mixture Model</a>, <a href="https://publications.waset.org/search?q=Independent%20Component%0AAnalysis" title=" Independent Component Analysis"> Independent Component Analysis</a>, <a href="https://publications.waset.org/search?q=Segmentation" title=" Segmentation"> Segmentation</a>, <a href="https://publications.waset.org/search?q=Unsupervised%20Classification." title=" Unsupervised Classification."> Unsupervised Classification.</a> </p> <a href="https://publications.waset.org/4391/unsupervised-texture-classification-and-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4391/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4391/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4391/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4391/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4391/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4391/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4391/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4391/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4391/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4391/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4391.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1593</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9264</span> A Hybrid GMM/SVM System for Text Independent Speaker Identification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rafik%20Djemili">Rafik Djemili</a>, <a href="https://publications.waset.org/search?q=Mouldi%20Bedda"> Mouldi Bedda</a>, <a href="https://publications.waset.org/search?q=Hocine%20Bourouba"> Hocine Bourouba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Speaker%20identification" title="Speaker identification">Speaker identification</a>, <a href="https://publications.waset.org/search?q=Gaussian%20mixture%20model%0A%28GMM%29" title=" Gaussian mixture model (GMM)"> Gaussian mixture model (GMM)</a>, <a href="https://publications.waset.org/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a>, <a href="https://publications.waset.org/search?q=hybrid%20GMM%2FSVM." title=" hybrid GMM/SVM."> hybrid GMM/SVM.</a> </p> <a href="https://publications.waset.org/12564/a-hybrid-gmmsvm-system-for-text-independent-speaker-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12564/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12564/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12564/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12564/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12564/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12564/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12564/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12564/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12564/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12564/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2237</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9263</span> An Evaluation of Algorithms for Single-Echo Biosonar Target Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Turgay%20Temel">Turgay Temel</a>, <a href="https://publications.waset.org/search?q=John%20Hallam">John Hallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=neuro-spike%20coding" title=" neuro-spike coding"> neuro-spike coding</a>, <a href="https://publications.waset.org/search?q=non-parametricmodel" title=" non-parametricmodel"> non-parametricmodel</a>, <a href="https://publications.waset.org/search?q=parametric%20model" title=" parametric model"> parametric model</a>, <a href="https://publications.waset.org/search?q=Gaussian%20mixture" title=" Gaussian mixture"> Gaussian mixture</a>, <a href="https://publications.waset.org/search?q=EM%20algorithm." title=" EM algorithm."> EM algorithm.</a> </p> <a href="https://publications.waset.org/972/an-evaluation-of-algorithms-for-single-echo-biosonar-target-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/972/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/972/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/972/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/972/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/972/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/972/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/972/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/972/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/972/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/972/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/972.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1670</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9262</span> Color Image Segmentation using Adaptive Spatial Gaussian Mixture Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.Sujaritha">M.Sujaritha</a>, <a href="https://publications.waset.org/search?q=S.%20Annadurai"> S. Annadurai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%3B%20Spatial" title="Adaptive; Spatial">Adaptive; Spatial</a>, <a href="https://publications.waset.org/search?q=Mixture%20model" title=" Mixture model"> Mixture model</a>, <a href="https://publications.waset.org/search?q=Segmentation" title=" Segmentation"> Segmentation</a>, <a href="https://publications.waset.org/search?q=Color." title=" Color."> Color.</a> </p> <a href="https://publications.waset.org/7706/color-image-segmentation-using-adaptive-spatial-gaussian-mixture-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7706/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7706/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7706/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7706/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7706/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7706/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7706/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7706/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7706/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7706/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7706.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2498</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9261</span> Analysis of Driving Conditions and Preferred Media on Diversion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yoon-Hyuk%20Choi">Yoon-Hyuk Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies on the distribution of traffic demands have been proceeding by providing traffic information for reducing greenhouse gases and reinforcing the road's competitiveness in the transport section, however, since it is preferentially required the extensive studies on the driver's behavior changing routes and its influence factors, this study has been developed a discriminant model for changing routes considering driving conditions including traffic conditions of roads and driver's preferences for information media. It is divided into three groups depending on driving conditions in group classification with the CART analysis, which is statistically meaningful. And the extent that driving conditions and preferred media affect a route change is examined through a discriminant analysis, and it is developed a discriminant model equation to predict a route change. As a result of building the discriminant model equation, it is shown that driving conditions affect a route change much more, the entire discriminant hit ratio is derived as 64.2%, and this discriminant equation shows high discriminant ability more than a certain degree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=CART%20analysis" title="CART analysis">CART analysis</a>, <a href="https://publications.waset.org/search?q=Diversion" title=" Diversion"> Diversion</a>, <a href="https://publications.waset.org/search?q=Discriminant%20model" title=" Discriminant model"> Discriminant model</a>, <a href="https://publications.waset.org/search?q=Driving%20conditions" title=" Driving conditions"> Driving conditions</a>, <a href="https://publications.waset.org/search?q=and%20preferred%20media" title=" and preferred media"> and preferred media</a> </p> <a href="https://publications.waset.org/5884/analysis-of-driving-conditions-and-preferred-media-on-diversion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5884/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5884/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5884/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5884/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5884/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5884/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5884/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5884/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5884/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5884/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1054</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9260</span> An Iterative Algorithm for KLDA Classifier </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=D.N.%20Zheng">D.N. Zheng</a>, <a href="https://publications.waset.org/search?q=J.X.%20Wang"> J.X. Wang</a>, <a href="https://publications.waset.org/search?q=Y.N.%20Zhao"> Y.N. Zhao</a>, <a href="https://publications.waset.org/search?q=Z.H.%20Yang"> Z.H. Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experiments show that it has a comparable performance with SVM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Linear%20discriminant%20analysis%20%28LDA%29" title="Linear discriminant analysis (LDA)">Linear discriminant analysis (LDA)</a>, <a href="https://publications.waset.org/search?q=kernel%20LDA%0D%0A%28KLDA%29" title=" kernel LDA (KLDA)"> kernel LDA (KLDA)</a>, <a href="https://publications.waset.org/search?q=conjugate%20gradient%20algorithm" title=" conjugate gradient algorithm"> conjugate gradient algorithm</a>, <a href="https://publications.waset.org/search?q=nonlinear%20discriminant%20classifier." title=" nonlinear discriminant classifier."> nonlinear discriminant classifier.</a> </p> <a href="https://publications.waset.org/4549/an-iterative-algorithm-for-klda-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4549/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4549/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4549/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4549/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4549/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4549/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4549/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4549/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4549/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4549/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1959</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9259</span> Morphometric Analysis of Tor tambroides by Stepwise Discriminant and Neural Network Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Pollar">M. Pollar</a>, <a href="https://publications.waset.org/search?q=M.%20Jaroensutasinee"> M. Jaroensutasinee</a>, <a href="https://publications.waset.org/search?q=K.%20Jaroensutasinee"> K. Jaroensutasinee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The population structure of the Tor tambroides was investigated with morphometric data (i.e. morphormetric measurement and truss measurement). A morphometric analysis was conducted to compare specimens from three waterfalls: Sunanta, Nan Chong Fa and Wang Muang waterfalls at Khao Nan National Park, Nakhon Si Thammarat, Southern Thailand. The results of stepwise discriminant analysis on seven morphometric variables and 21 truss variables per individual were the same as from a neural network. Fish from three waterfalls were separated into three groups based on their morphometric measurements. The morphometric data shows that the nerual network model performed better than the stepwise discriminant analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Morphometric" title="Morphometric">Morphometric</a>, <a href="https://publications.waset.org/search?q=Tor%20tambroides" title=" Tor tambroides"> Tor tambroides</a>, <a href="https://publications.waset.org/search?q=Stepwise%0ADiscriminant%20Analysis" title=" Stepwise Discriminant Analysis "> Stepwise Discriminant Analysis </a>, <a href="https://publications.waset.org/search?q=Neural%20Network%20Analysis." title=" Neural Network Analysis."> Neural Network Analysis.</a> </p> <a href="https://publications.waset.org/7530/morphometric-analysis-of-tor-tambroides-by-stepwise-discriminant-and-neural-network-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7530/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7530/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7530/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7530/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7530/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7530/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7530/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7530/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7530/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7530/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2150</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9258</span> Adaptive Gaussian Mixture Model for Skin Color Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Reza%20Hassanpour">Reza Hassanpour</a>, <a href="https://publications.waset.org/search?q=Asadollah%20Shahbahrami"> Asadollah Shahbahrami</a>, <a href="https://publications.waset.org/search?q=Stephan%20Wong"> Stephan Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skin color based tracking techniques often assume a static skin color model obtained either from an offline set of library images or the first few frames of a video stream. These models can show a weak performance in presence of changing lighting or imaging conditions. We propose an adaptive skin color model based on the Gaussian mixture model to handle the changing conditions. Initial estimation of the number and weights of skin color clusters are obtained using a modified form of the general Expectation maximization algorithm, The model adapts to changes in imaging conditions and refines the model parameters dynamically using spatial and temporal constraints. Experimental results show that the method can be used in effectively tracking of hand and face regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Face%20detection" title="Face detection">Face detection</a>, <a href="https://publications.waset.org/search?q=Segmentation" title=" Segmentation"> Segmentation</a>, <a href="https://publications.waset.org/search?q=Tracking" title=" Tracking"> Tracking</a>, <a href="https://publications.waset.org/search?q=Gaussian%0AMixture%20Model" title=" Gaussian Mixture Model"> Gaussian Mixture Model</a>, <a href="https://publications.waset.org/search?q=Adaptation." title=" Adaptation."> Adaptation.</a> </p> <a href="https://publications.waset.org/4014/adaptive-gaussian-mixture-model-for-skin-color-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4014/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4014/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4014/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4014/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4014/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4014/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4014/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4014/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4014/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4014/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2415</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9257</span> Discriminant Analysis as a Function of Predictive Learning to Select Evolutionary Algorithms in Intelligent Transportation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jorge%20A.%20Ruiz-Vanoye">Jorge A. Ruiz-Vanoye</a>, <a href="https://publications.waset.org/search?q=Ocotl%C3%A1n%20D%C3%ADaz-Parra"> Ocotlán Díaz-Parra</a>, <a href="https://publications.waset.org/search?q=Alejandro%20Fuentes-Penna"> Alejandro Fuentes-Penna</a>, <a href="https://publications.waset.org/search?q=Daniel%20V%C3%A9lez-D%C3%ADaz"> Daniel Vélez-Díaz</a>, <a href="https://publications.waset.org/search?q=Edith%20Olaco%20Garc%C3%ADa"> Edith Olaco García</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intelligent%20transportation%20systems" title="Intelligent transportation systems">Intelligent transportation systems</a>, <a href="https://publications.waset.org/search?q=data-mining%20techniques" title=" data-mining techniques"> data-mining techniques</a>, <a href="https://publications.waset.org/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a>, <a href="https://publications.waset.org/search?q=machine%20learning." title=" machine learning. "> machine learning. </a> </p> <a href="https://publications.waset.org/10004073/discriminant-analysis-as-a-function-of-predictive-learning-to-select-evolutionary-algorithms-in-intelligent-transportation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004073/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004073/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004073/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004073/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004073/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004073/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004073/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004073/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004073/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004073/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1547</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9256</span> Recursive Algorithms for Image Segmentation Based on a Discriminant Criterion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Bing-Fei%20Wu">Bing-Fei Wu</a>, <a href="https://publications.waset.org/search?q=Yen-Lin%20Chen"> Yen-Lin Chen</a>, <a href="https://publications.waset.org/search?q=Chung-Cheng%20Chiu"> Chung-Cheng Chiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this study, a new criterion for determining the number of classes an image should be segmented is proposed. This criterion is based on discriminant analysis for measuring the separability among the segmented classes of pixels. Based on the new discriminant criterion, two algorithms for recursively segmenting the image into determined number of classes are proposed. The proposed methods can automatically and correctly segment objects with various illuminations into separated images for further processing. Experiments on the extraction of text strings from complex document images demonstrate the effectiveness of the proposed methods.1</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/search?q=multilevel%20thresholding" title=" multilevel thresholding"> multilevel thresholding</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a> </p> <a href="https://publications.waset.org/13494/recursive-algorithms-for-image-segmentation-based-on-a-discriminant-criterion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13494/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13494/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13494/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13494/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13494/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13494/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13494/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13494/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13494/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13494/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13494.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2036</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9255</span> Evaluation of Algorithms for Sequential Decision in Biosonar Target Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Turgay%20Temel">Turgay Temel</a>, <a href="https://publications.waset.org/search?q=John%20Hallam"> John Hallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A sequential decision problem, based on the task ofidentifying the species of trees given acoustic echo data collectedfrom them, is considered with well-known stochastic classifiers,including single and mixture Gaussian models. Echoes are processedwith a preprocessing stage based on a model of mammalian cochlearfiltering, using a new discrete low-pass filter characteristic. Stoppingtime performance of the sequential decision process is evaluated andcompared. It is observed that the new low pass filter processingresults in faster sequential decisions.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Classification" title="Classification">Classification</a>, <a href="https://publications.waset.org/search?q=neuro-spike%20coding" title=" neuro-spike coding"> neuro-spike coding</a>, <a href="https://publications.waset.org/search?q=parametricmodel" title=" parametricmodel"> parametricmodel</a>, <a href="https://publications.waset.org/search?q=Gaussian%20mixture%20with%20EM%20algorithm" title=" Gaussian mixture with EM algorithm"> Gaussian mixture with EM algorithm</a>, <a href="https://publications.waset.org/search?q=sequential%20decision." title=" sequential decision."> sequential decision.</a> </p> <a href="https://publications.waset.org/8331/evaluation-of-algorithms-for-sequential-decision-in-biosonar-target-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8331/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8331/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8331/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8331/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8331/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8331/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8331/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8331/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8331/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8331/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1547</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9254</span> Learning the Dynamics of Articulated Tracked Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/search?q=Fiora%20Pirri"> Fiora Pirri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/search?q=Gaussian%20processes" title=" Gaussian processes"> Gaussian processes</a>, <a href="https://publications.waset.org/search?q=robot%20control%0D%0Alearning" title=" robot control learning"> robot control learning</a>, <a href="https://publications.waset.org/search?q=tracked%20vehicles." title=" tracked vehicles."> tracked vehicles.</a> </p> <a href="https://publications.waset.org/10004630/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004630/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004630/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004630/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004630/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004630/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004630/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004630/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004630/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004630/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004630/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1783</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9253</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Human%20action%20recognition" title="Human action recognition">Human action recognition</a>, <a href="https://publications.waset.org/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/search?q=Prior%20distribution%20and%20approximate%20posterior%20distribution" title=" Prior distribution and approximate posterior distribution"> Prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/search?q=KTH%20dataset." title=" KTH dataset."> KTH dataset.</a> </p> <a href="https://publications.waset.org/10005884/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005884/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005884/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005884/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005884/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005884/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005884/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005884/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005884/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005884/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005884/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1006</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9252</span> On the Multiplicity of Discriminants of Relative Quadratic Extensions of Quintic Fields</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Schehrazad%20Selmane">Schehrazad Selmane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>According to Hermite there exists only a finite number of number fields having a given degree, and a given value of the discriminant, nevertheless this number is not known generally. The determination of a maximum number of number fields of degree 10 having a given discriminant that contain a subfield of degree 5 having a fixed class number, narrow class number and Galois group is the purpose of this work. The constructed lists of the first coincidences of 52 (resp. 50, 40, 48, 22, 6) nonisomorphic number fields with same discriminant of degree 10 of signature (6,2) (resp. (4,3), (8,1), (2,4), (0,5), (10,0)) containing a quintic field. For each field in the lists, we indicate its discriminant, the discriminant of its subfield, a relative polynomial generating the field over its quintic field and its relative discriminant, the corresponding polynomial over Q and its Galois closure are presented with concluding remarks.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Discriminant" title="Discriminant">Discriminant</a>, <a href="https://publications.waset.org/search?q=nonisomorphic%20fields" title=" nonisomorphic fields"> nonisomorphic fields</a>, <a href="https://publications.waset.org/search?q=quintic%20fields" title=" quintic fields"> quintic fields</a>, <a href="https://publications.waset.org/search?q=relative%20quadratic%20extensions." title=" relative quadratic extensions."> relative quadratic extensions.</a> </p> <a href="https://publications.waset.org/16338/on-the-multiplicity-of-discriminants-of-relative-quadratic-extensions-of-quintic-fields" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16338/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16338/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16338/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16338/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16338/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16338/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16338/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16338/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16338/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16338/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16338.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1462</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9251</span> An Alternative Method for Generating Almost Infinite Sequence of Gaussian Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nyah%20C.%20Temaneh">Nyah C. Temaneh</a>, <a href="https://publications.waset.org/search?q=F.%20A.%20Phiri"> F. A. Phiri</a>, <a href="https://publications.waset.org/search?q=E.%20Ruhunga"> E. Ruhunga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the well known methods for generating Gaussian variables require at least one standard uniform distributed value, for each Gaussian variable generated. The length of the random number generator therefore, limits the number of independent Gaussian distributed variables that can be generated meanwhile the statistical solution of complex systems requires a large number of random numbers for their statistical analysis. We propose an alternative simple method of generating almost infinite number of Gaussian distributed variables using a limited number of standard uniform distributed random numbers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20variable" title="Gaussian variable">Gaussian variable</a>, <a href="https://publications.waset.org/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/search?q=simulation%20ofCommunication%20Network" title=" simulation ofCommunication Network"> simulation ofCommunication Network</a>, <a href="https://publications.waset.org/search?q=Random%20numbers." title=" Random numbers."> Random numbers.</a> </p> <a href="https://publications.waset.org/10547/an-alternative-method-for-generating-almost-infinite-sequence-of-gaussian-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10547/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10547/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10547/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10547/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10547/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10547/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10547/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10547/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10547/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10547/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10547.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1473</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9250</span> Learning an Overcomplete Dictionary using a Cauchy Mixture Model for Sparse Decay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=E.%20S.%20Gower">E. S. Gower</a>, <a href="https://publications.waset.org/search?q=M.%20O.%20J.%20Hawksford"> M. O. J. Hawksford</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm-s robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=expectation-maximization" title="expectation-maximization">expectation-maximization</a>, <a href="https://publications.waset.org/search?q=Pitman%20estimator" title=" Pitman estimator"> Pitman estimator</a>, <a href="https://publications.waset.org/search?q=sparsedecomposition" title=" sparsedecomposition"> sparsedecomposition</a> </p> <a href="https://publications.waset.org/10330/learning-an-overcomplete-dictionary-using-a-cauchy-mixture-model-for-sparse-decay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10330/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10330/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10330/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10330/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10330/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10330/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10330/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10330/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10330/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10330/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1949</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9249</span> Optimal and Generalized Multiple Descriptions Image Coding Transform in the Wavelet Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Bahi%20brahim">Bahi brahim</a>, <a href="https://publications.waset.org/search?q=El%20hassane%20Ibn%20Elhaj"> El hassane Ibn Elhaj</a>, <a href="https://publications.waset.org/search?q=Driss%20Aboutajdine"> Driss Aboutajdine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we propose a Multiple Description Image Coding(MDIC) scheme to generate two compressed and balanced rates descriptions in the wavelet domain (Daubechies biorthogonal (9, 7) wavelet) using pairwise correlating transform optimal and application method for Generalized Multiple Description Coding (GMDC) to image coding in the wavelet domain. The GMDC produces statistically correlated streams such that lost streams can be estimated from the received data. Our performance test shown that the proposed method gives more improvement and good quality of the reconstructed image when the wavelet coefficients are normalized by Gaussian Scale Mixture (GSM) model then the Gaussian one ,.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Multiple%20description%20coding%20%28MDC%29" title="Multiple description coding (MDC)">Multiple description coding (MDC)</a>, <a href="https://publications.waset.org/search?q=gaussian%20scale%20mixture%20%28GSM%29%20model" title=" gaussian scale mixture (GSM) model"> gaussian scale mixture (GSM) model</a>, <a href="https://publications.waset.org/search?q=joint%20source-channel%20coding" title=" joint source-channel coding"> joint source-channel coding</a>, <a href="https://publications.waset.org/search?q=pairwise%20correlating%20transform" title=" pairwise correlating transform"> pairwise correlating transform</a>, <a href="https://publications.waset.org/search?q=GMDCT." title=" GMDCT."> GMDCT.</a> </p> <a href="https://publications.waset.org/8699/optimal-and-generalized-multiple-descriptions-image-coding-transform-in-the-wavelet-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8699/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8699/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8699/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8699/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8699/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8699/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8699/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8699/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8699/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8699/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8699.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1618</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9248</span> Region Segmentation based on Gaussian Dirichlet Process Mixture Model and its Application to 3D Geometric Stricture Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jonghyun%20Park">Jonghyun Park</a>, <a href="https://publications.waset.org/search?q=Soonyoung%20Park"> Soonyoung Park</a>, <a href="https://publications.waset.org/search?q=Sanggyun%20Kim"> Sanggyun Kim</a>, <a href="https://publications.waset.org/search?q=Wanhyun%20Cho"> Wanhyun Cho</a>, <a href="https://publications.waset.org/search?q=Sunworl%20Kim"> Sunworl Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. So, It is important to segment ROI (region of interest) from input scenes as a preprocessing step for geometric stricture detection in 3D scene. In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting and Dirichlet process mixture model to a image segmentation. The tensor voting is used based on the fact that homogeneous region in an image are usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. The proposed approach is a novel nonparametric Bayesian segmentation method using Gaussian Dirichlet process mixture model to automatically segment various natural scenes. Finally, our method can label regions of the input image into coarse categories: “ground", “sky", and “vertical" for 3D application. The experimental results show that our method successfully segments coarse regions in many complex natural scene images for 3D.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Region%20segmentation" title="Region segmentation">Region segmentation</a>, <a href="https://publications.waset.org/search?q=tensor%20voting" title=" tensor voting"> tensor voting</a>, <a href="https://publications.waset.org/search?q=image-based%203D" title=" image-based 3D"> image-based 3D</a>, <a href="https://publications.waset.org/search?q=geometric%20structure" title=" geometric structure"> geometric structure</a>, <a href="https://publications.waset.org/search?q=Gaussian%20Dirichlet%20process%20mixture%20model" title=" Gaussian Dirichlet process mixture model"> Gaussian Dirichlet process mixture model</a> </p> <a href="https://publications.waset.org/7008/region-segmentation-based-on-gaussian-dirichlet-process-mixture-model-and-its-application-to-3d-geometric-stricture-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7008/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7008/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7008/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7008/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7008/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7008/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7008/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7008/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7008/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7008/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1891</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9247</span> A Non-Parametric Based Mapping Algorithm for Use in Audio Fingerprinting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Analise%20Borg">Analise Borg</a>, <a href="https://publications.waset.org/search?q=Paul%20Micallef"> Paul Micallef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Over the past few years, the online multimedia collection has grown at a fast pace. Several companies showed interest to study the different ways to organise the amount of audio information without the need of human intervention to generate metadata. In the past few years, many applications have emerged on the market which are capable of identifying a piece of music in a short time. Different audio effects and degradation make it much harder to identify the unknown piece. In this paper, an audio fingerprinting system which makes use of a non-parametric based algorithm is presented. Parametric analysis is also performed using Gaussian Mixture Models (GMMs). The feature extraction methods employed are the Mel Spectrum Coefficients and the MPEG-7 basic descriptors. Bin numbers replaced the extracted feature coefficients during the non-parametric modelling. The results show that nonparametric analysis offer potential results as the ones mentioned in the literature.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Audio%20fingerprinting" title="Audio fingerprinting">Audio fingerprinting</a>, <a href="https://publications.waset.org/search?q=mapping%20algorithm" title=" mapping algorithm"> mapping algorithm</a>, <a href="https://publications.waset.org/search?q=Gaussian%0D%0AMixture%20Models" title=" Gaussian Mixture Models"> Gaussian Mixture Models</a>, <a href="https://publications.waset.org/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/search?q=MPEG-7." title=" MPEG-7."> MPEG-7.</a> </p> <a href="https://publications.waset.org/10000899/a-non-parametric-based-mapping-algorithm-for-use-in-audio-fingerprinting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000899/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000899/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000899/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000899/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000899/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000899/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000899/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000899/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000899/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000899/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000899.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2285</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9246</span> Puff Noise Detection and Cancellation for Robust Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sangjun%20Park">Sangjun Park</a>, <a href="https://publications.waset.org/search?q=Jungpyo%20Hong"> Jungpyo Hong</a>, <a href="https://publications.waset.org/search?q=Byung-Ok%20Kang"> Byung-Ok Kang</a>, <a href="https://publications.waset.org/search?q=Yun-keun%20Lee"> Yun-keun Lee</a>, <a href="https://publications.waset.org/search?q=Minsoo%20Hahn"> Minsoo Hahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an algorithm for detecting and attenuating puff noises frequently generated under the mobile environment is proposed. As a baseline system, puff detection system is designed based on Gaussian Mixture Model (GMM), and 39th Mel Frequency Cepstral Coefficient (MFCC) is extracted as feature parameters. To improve the detection performance, effective acoustic features for puff detection are proposed. In addition, detected puff intervals are attenuated by high-pass filtering. The speech recognition rate was measured for evaluation and confusion matrix and ROC curve are used to confirm the validity of the proposed system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20mixture%20model" title="Gaussian mixture model">Gaussian mixture model</a>, <a href="https://publications.waset.org/search?q=puff%20detection%20and%0Acancellation" title=" puff detection and cancellation"> puff detection and cancellation</a>, <a href="https://publications.waset.org/search?q=speech%20enhancement." title=" speech enhancement."> speech enhancement.</a> </p> <a href="https://publications.waset.org/10021/puff-noise-detection-and-cancellation-for-robust-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10021/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10021/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10021/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10021/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10021/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10021/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10021/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10021/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10021/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10021/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2234</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9245</span> Bidirectional Discriminant Supervised Locality Preserving Projection for Face Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yiqin%20Lin">Yiqin Lin</a>, <a href="https://publications.waset.org/search?q=Wenbo%20Li"> Wenbo Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dimensionality reduction and feature extraction are of crucial importance for achieving high efficiency in manipulating the high dimensional data. Two-dimensional discriminant locality preserving projection (2D-DLPP) and two-dimensional discriminant supervised LPP (2D-DSLPP) are two effective two-dimensional projection methods for dimensionality reduction and feature extraction of face image matrices. Since 2D-DLPP and 2D-DSLPP preserve the local structure information of the original data and exploit the discriminant information, they usually have good recognition performance. However, 2D-DLPP and 2D-DSLPP only employ single-sided projection, and thus the generated low dimensional data matrices have still many features. In this paper, by combining the discriminant supervised LPP with the bidirectional projection, we propose the bidirectional discriminant supervised LPP (BDSLPP). The left and right projection matrices for BDSLPP can be computed iteratively. Experimental results show that the proposed BDSLPP achieves higher recognition accuracy than 2D-DLPP, 2D-DSLPP, and bidirectional discriminant LPP (BDLPP). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Face%20recognition" title="Face recognition">Face recognition</a>, <a href="https://publications.waset.org/search?q=dimension%20reduction" title=" dimension reduction"> dimension reduction</a>, <a href="https://publications.waset.org/search?q=locality%0D%0Apreserving%20projection" title=" locality preserving projection"> locality preserving projection</a>, <a href="https://publications.waset.org/search?q=discriminant%20information" title=" discriminant information"> discriminant information</a>, <a href="https://publications.waset.org/search?q=bidirectional%0D%0Aprojection." title=" bidirectional projection."> bidirectional projection.</a> </p> <a href="https://publications.waset.org/10010925/bidirectional-discriminant-supervised-locality-preserving-projection-for-face-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010925/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010925/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010925/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010925/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010925/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010925/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010925/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010925/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010925/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010925/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">690</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9244</span> Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohammed%20Rziza">Mohammed Rziza</a>, <a href="https://publications.waset.org/search?q=Mohamed%20El%20Aroussi"> Mohamed El Aroussi</a>, <a href="https://publications.waset.org/search?q=Mohammed%20El%20Hassouni"> Mohammed El Hassouni</a>, <a href="https://publications.waset.org/search?q=Sanaa%20Ghouzali"> Sanaa Ghouzali</a>, <a href="https://publications.waset.org/search?q=Driss%20Aboutajdine"> Driss Aboutajdine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Curvelet" title="Curvelet">Curvelet</a>, <a href="https://publications.waset.org/search?q=Linear%20Discriminant%20Analysis%20%28LDA%29" title=" Linear Discriminant Analysis (LDA) "> Linear Discriminant Analysis (LDA) </a>, <a href="https://publications.waset.org/search?q=Contourlet" title=" Contourlet"> Contourlet</a>, <a href="https://publications.waset.org/search?q=Discreet%20Wavelet%20Transform" title="Discreet Wavelet Transform">Discreet Wavelet Transform</a>, <a href="https://publications.waset.org/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/search?q=Block-based%20analysis" title=" Block-based analysis"> Block-based analysis</a>, <a href="https://publications.waset.org/search?q=face%20recognition%20%28FR%29." title="face recognition (FR).">face recognition (FR).</a> </p> <a href="https://publications.waset.org/6439/local-curvelet-based-classification-using-linear-discriminant-analysis-for-face-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6439/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6439/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6439/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6439/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6439/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6439/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6439/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6439/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6439/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6439/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1808</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9243</span> Journey on Image Clustering Based on Color Composition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Achmad%20Nizar%20Hidayanto">Achmad Nizar Hidayanto</a>, <a href="https://publications.waset.org/search?q=Elisabeth%20Martha%20Koeanan"> Elisabeth Martha Koeanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image clustering is a process of grouping images based on their similarity. The image clustering usually uses the color component, texture, edge, shape, or mixture of two components, etc. This research aims to explore image clustering using color composition. In order to complete this image clustering, three main components should be considered, which are color space, image representation (feature extraction), and clustering method itself. We aim to explore which composition of these factors will produce the best clustering results by combining various techniques from the three components. The color spaces use RGB, HSV, and L*a*b* method. The image representations use Histogram and Gaussian Mixture Model (GMM), whereas the clustering methods use KMeans and Agglomerative Hierarchical Clustering algorithm. The results of the experiment show that GMM representation is better combined with RGB and L*a*b* color space, whereas Histogram is better combined with HSV. The experiments also show that K-Means is better than Agglomerative Hierarchical for images clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20clustering" title="Image clustering">Image clustering</a>, <a href="https://publications.waset.org/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/search?q=RGB" title=" RGB"> RGB</a>, <a href="https://publications.waset.org/search?q=HSV" title=" HSV"> HSV</a>, <a href="https://publications.waset.org/search?q=L%2Aa%2Ab%2A" title="L*a*b*">L*a*b*</a>, <a href="https://publications.waset.org/search?q=Gaussian%20Mixture%20Model%20%28GMM%29" title=" Gaussian Mixture Model (GMM)"> Gaussian Mixture Model (GMM)</a>, <a href="https://publications.waset.org/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/search?q=Agglomerative%20Hierarchical%20Clustering%20%28AHC%29" title="Agglomerative Hierarchical Clustering (AHC)">Agglomerative Hierarchical Clustering (AHC)</a>, <a href="https://publications.waset.org/search?q=K-Means" title=" K-Means"> K-Means</a>, <a href="https://publications.waset.org/search?q=Expectation-Maximization%20%28EM%29." title="Expectation-Maximization (EM).">Expectation-Maximization (EM).</a> </p> <a href="https://publications.waset.org/11825/journey-on-image-clustering-based-on-color-composition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11825/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11825/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11825/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11825/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11825/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11825/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11825/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11825/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11825/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11825/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11825.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2206</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9242</span> An Approach for Reducing the Computational Complexity of LAMSTAR Intrusion Detection System using Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=V.%20Venkatachalam">V. Venkatachalam</a>, <a href="https://publications.waset.org/search?q=S.%20Selvan"> S. Selvan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The security of computer networks plays a strategic role in modern computer systems. Intrusion Detection Systems (IDS) act as the 'second line of defense' placed inside a protected network, looking for known or potential threats in network traffic and/or audit data recorded by hosts. We developed an Intrusion Detection System using LAMSTAR neural network to learn patterns of normal and intrusive activities, to classify observed system activities and compared the performance of LAMSTAR IDS with other classification techniques using 5 classes of KDDCup99 data. LAMSAR IDS gives better performance at the cost of high Computational complexity, Training time and Testing time, when compared to other classification techniques (Binary Tree classifier, RBF classifier, Gaussian Mixture classifier). we further reduced the Computational Complexity of LAMSTAR IDS by reducing the dimension of the data using principal component analysis which in turn reduces the training and testing time with almost the same performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Binary%20Tree%20Classifier" title="Binary Tree Classifier">Binary Tree Classifier</a>, <a href="https://publications.waset.org/search?q=Gaussian%20Mixture" title=" Gaussian Mixture"> Gaussian Mixture</a>, <a href="https://publications.waset.org/search?q=IntrusionDetection%20System" title=" IntrusionDetection System"> IntrusionDetection System</a>, <a href="https://publications.waset.org/search?q=LAMSTAR" title=" LAMSTAR"> LAMSTAR</a>, <a href="https://publications.waset.org/search?q=Radial%20Basis%20Function." title=" Radial Basis Function."> Radial Basis Function.</a> </p> <a href="https://publications.waset.org/1858/an-approach-for-reducing-the-computational-complexity-of-lamstar-intrusion-detection-system-using-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1858/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1858/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1858/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1858/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1858/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1858/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1858/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1858/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1858/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1858/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1858.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1750</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9241</span> Base Change for Fisher Metrics: Case of the q−Gaussian Inverse Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Gabriel%20I.%20Loaiza%20O.">Gabriel I. Loaiza O.</a>, <a href="https://publications.waset.org/search?q=Carlos%20A.%20Cadavid%20M."> Carlos A. Cadavid M.</a>, <a href="https://publications.waset.org/search?q=Juan%20C.%20Arango%20P."> Juan C. Arango P.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>It is known that the Riemannian manifold determined by the family of inverse Gaussian distributions endowed with the Fisher metric has negative constant curvature κ = −1/2 , as does the family of usual Gaussian distributions. In the present paper, firstly we arrive at this result by following a different path, much simpler than the previous ones. We first put the family in exponential form, thus endowing the family with a new set of parameters, or coordinates, θ1, θ2; then we determine the matrix of the Fisher metric in terms of these parameters; and finally we compute this matrix in the original parameters. Secondly, we define the Inverse q−Gaussian distribution family (q < 3), as the family obtained by replacing the usual exponential function by the Tsallis q−exponential function in the expression for the Inverse Gaussian distribution, and observe that it supports two possible geometries, the Fisher and the q−Fisher geometry. And finally, we apply our strategy to obtain results about the Fisher and q−Fisher geometry of the Inverse q−Gaussian distribution family, similar to the ones obtained in the case of the Inverse Gaussian distribution family. </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Base%20of%20Changes" title="Base of Changes">Base of Changes</a>, <a href="https://publications.waset.org/search?q=Information%20Geometry" title=" Information Geometry"> Information Geometry</a>, <a href="https://publications.waset.org/search?q=Inverse%0D%0AGaussian%20distribution" title=" Inverse Gaussian distribution"> Inverse Gaussian distribution</a>, <a href="https://publications.waset.org/search?q=Inverse%20q-Gaussian%20distribution" title=" Inverse q-Gaussian distribution"> Inverse q-Gaussian distribution</a>, <a href="https://publications.waset.org/search?q=Statistical%0D%0AManifolds." title=" Statistical Manifolds."> Statistical Manifolds.</a> </p> <a href="https://publications.waset.org/10012676/base-change-for-fisher-metrics-case-of-the-qgaussian-inverse-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012676/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012676/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012676/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012676/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012676/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012676/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012676/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012676/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012676/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012676/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012676.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9240</span> The Classification Performance in Parametric and Nonparametric Discriminant Analysis for a Class- Unbalanced Data of Diabetes Risk Groups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lily%20Ingsrisawang">Lily Ingsrisawang</a>, <a href="https://publications.waset.org/search?q=Tasanee%20Nacharoen"> Tasanee Nacharoen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problems arising from unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many researchers have found that the performance of existing classifiers tends to be biased towards the majority class. The k-nearest neighbors’ nonparametric discriminant analysis is a method that was proposed for classifying unbalanced classes with good performance. In this study, the methods of discriminant analysis are of interest in investigating misclassification error rates for classimbalanced data of three diabetes risk groups. The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification of class-imbalanced data of diabetes risk groups. Data from a project maintaining healthy conditions for 599 employees of a government hospital in Bangkok were obtained for the classification problem. The employees were divided into three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data including the variables of diabetes risk group, age, gender, blood glucose, and BMI were analyzed and bootstrapped for 50 and 100 samples, 599 observations per sample, for additional estimation of the misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples showed nonnormality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. Searching the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions of (0.90:0.05:0.05), (0.80: 0.10: 0.10) and (0.70, 0.15, 0.15). The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k=3 or k=4 and the defined prior probabilities of non-risk: risk: diabetic as 0.90: 0.05:0.05 or 0.80:0.10:0.10 gave the smallest error rate of misclassification. The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bootstrap" title="Bootstrap">Bootstrap</a>, <a href="https://publications.waset.org/search?q=diabetes%20risk%20groups" title=" diabetes risk groups"> diabetes risk groups</a>, <a href="https://publications.waset.org/search?q=error%20rate" title=" error rate"> error rate</a>, <a href="https://publications.waset.org/search?q=k-nearest%0D%0Aneighbors." title=" k-nearest neighbors."> k-nearest neighbors.</a> </p> <a href="https://publications.waset.org/10001482/the-classification-performance-in-parametric-and-nonparametric-discriminant-analysis-for-a-class-unbalanced-data-of-diabetes-risk-groups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001482/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001482/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001482/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001482/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001482/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001482/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001482/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001482/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001482/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001482/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001482.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2008</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9239</span> An Extension of the Kratzel Function and Associated Inverse Gaussian Probability Distribution Occurring in Reliability Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20K.%20Saxena">R. K. Saxena</a>, <a href="https://publications.waset.org/search?q=Ravi%20Saxena"> Ravi Saxena</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In view of their importance and usefulness in reliability theory and probability distributions, several generalizations of the inverse Gaussian distribution and the Krtzel function are investigated in recent years. This has motivated the authors to introduce and study a new generalization of the inverse Gaussian distribution and the Krtzel function associated with a product of a Bessel function of the third kind )(zKQ and a Z - Fox-Wright generalized hyper geometric function introduced in this paper. The introduced function turns out to be a unified gamma-type function. Its incomplete forms are also discussed. Several properties of this gamma-type function are obtained. By means of this generalized function, we introduce a generalization of inverse Gaussian distribution, which is useful in reliability analysis, diffusion processes, and radio techniques etc. The inverse Gaussian distribution thus introduced also provides a generalization of the Krtzel function. Some basic statistical functions associated with this probability density function, such as moments, the Mellin transform, the moment generating function, the hazard rate function, and the mean residue life function are also obtained.KeywordsFox-Wright function, Inverse Gaussian distribution, Krtzel function & Bessel function of the third kind.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fox-Wright%20function" title="Fox-Wright function">Fox-Wright function</a>, <a href="https://publications.waset.org/search?q=Inverse%20Gaussian%20distribution" title=" Inverse Gaussian distribution"> Inverse Gaussian distribution</a>, <a href="https://publications.waset.org/search?q=Krtzel%20function%20%26%20Bessel%20function%20of%20the%20third%20kind." title=" Krtzel function & Bessel function of the third kind."> Krtzel function & Bessel function of the third kind.</a> </p> <a 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