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descriptor</title> <meta name="description" content="Search results for: feature descriptor"> <meta name="keywords" content="feature descriptor"> <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 Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/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="feature descriptor"> <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> 1602</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: feature descriptor</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1602</span> Fused Structure and Texture (FST) Features for Improved Pedestrian Detection </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussin%20K.%20Ragb">Hussin K. Ragb</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayan%20K.%20Asari"> Vijayan K. Asari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title="pedestrian detection">pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20congruency" title=" phase congruency"> phase congruency</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase" title=" local phase"> local phase</a>, <a href="https://publications.waset.org/abstracts/search?q=LBP%20features" title=" LBP features"> LBP features</a>, <a href="https://publications.waset.org/abstracts/search?q=CSLBP%20features" title=" CSLBP features"> CSLBP features</a>, <a href="https://publications.waset.org/abstracts/search?q=FST%20descriptor" title=" FST descriptor"> FST descriptor</a> </p> <a href="https://publications.waset.org/abstracts/36643/fused-structure-and-texture-fst-features-for-improved-pedestrian-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36643.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">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1601</span> Image Analysis for Obturator Foramen Based on Marker-controlled Watershed Segmentation and Zernike Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seda%20Sahin">Seda Sahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Emin%20Akata"> Emin Akata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20analysis" title="medical image analysis">medical image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20of%20bone%20structures%20on%20hip%20radiographs" title=" segmentation of bone structures on hip radiographs"> segmentation of bone structures on hip radiographs</a>, <a href="https://publications.waset.org/abstracts/search?q=marker-controlled%20watershed%20segmentation" title=" marker-controlled watershed segmentation"> marker-controlled watershed segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=zernike%20moment%20feature%20descriptor" title=" zernike moment feature descriptor"> zernike moment feature descriptor</a> </p> <a href="https://publications.waset.org/abstracts/31425/image-analysis-for-obturator-foramen-based-on-marker-controlled-watershed-segmentation-and-zernike-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31425.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">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1600</span> Diversity Indices as a Tool for Evaluating Quality of Water Ways</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadra%20Ahmed">Khadra Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Kheireldin"> Khaled Kheireldin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=planktons" title="planktons">planktons</a>, <a href="https://publications.waset.org/abstracts/search?q=diversity%20indices" title=" diversity indices"> diversity indices</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20index" title=" water quality index"> water quality index</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20ways" title=" water ways"> water ways</a> </p> <a href="https://publications.waset.org/abstracts/36684/diversity-indices-as-a-tool-for-evaluating-quality-of-water-ways" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36684.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">519</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1599</span> Computing Some Topological Descriptors of Single-Walled Carbon Nanotubes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Bahrami">Amir Bahrami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the fields of chemical graph theory, molecular topology, and mathematical chemistry, a topological index or a descriptor index also known as a connectivity index is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are numerical parameters of a graph which characterize its topology and are usually graph invariant. Topological indices are used for example in the development of quantitative structure-activity relationships (QSARs) in which the biological activity or other properties of molecules are correlated with their chemical structure. In this paper some descriptor index (descriptor index) of single-walled carbon nanotubes, is determined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemical%20graph%20theory" title="chemical graph theory">chemical graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20topology" title=" molecular topology"> molecular topology</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptor" title=" molecular descriptor"> molecular descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=single-walled%20carbon%20nanotubes" title=" single-walled carbon nanotubes"> single-walled carbon nanotubes</a> </p> <a href="https://publications.waset.org/abstracts/39279/computing-some-topological-descriptors-of-single-walled-carbon-nanotubes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39279.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">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1598</span> Enhancement Dynamic Cars Detection Based on Optimized HOG Descriptor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansouri%20Nabila">Mansouri Nabila</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Jemaa%20Yousra"> Ben Jemaa Yousra</a>, <a href="https://publications.waset.org/abstracts/search?q=Motamed%20Cina"> Motamed Cina</a>, <a href="https://publications.waset.org/abstracts/search?q=Watelain%20Eric"> Watelain Eric</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=car-detector" title="car-detector">car-detector</a>, <a href="https://publications.waset.org/abstracts/search?q=HOG" title=" HOG"> HOG</a>, <a href="https://publications.waset.org/abstracts/search?q=motion" title=" motion"> motion</a>, <a href="https://publications.waset.org/abstracts/search?q=computing%20time" title=" computing time"> computing time</a> </p> <a href="https://publications.waset.org/abstracts/40704/enhancement-dynamic-cars-detection-based-on-optimized-hog-descriptor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40704.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">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1597</span> A Computer-Aided System for Tooth Shade Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zuhal%20Kurt">Zuhal Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Meral%20Kurt"> Meral Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Bilge%20T.%20Bal"> Bilge T. Bal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Ozkan"> Kemal Ozkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shade matching and reproduction is the most important element of success in prosthetic dentistry. Until recently, shade matching procedure was implemented by dentists visual perception with the help of shade guides. Since many factors influence visual perception; tooth shade matching using visual devices (shade guides) is highly subjective and inconsistent. Subjective nature of this process has lead to the development of instrumental devices. Nowadays, colorimeters, spectrophotometers, spectroradiometers and digital image analysing systems are used for instrumental shade selection. Instrumental devices have advantages that readings are quantifiable, can obtain more rapidly and simply, objectively and precisely. However, these devices have noticeable drawbacks. For example, translucent structure and irregular surfaces of teeth lead to defects on measurement with these devices. Also between the results acquired by devices with different measurement principles may make inconsistencies. So, its obligatory to search for new methods for dental shade matching process. A computer-aided system device; digital camera has developed rapidly upon today. Currently, advances in image processing and computing have resulted in the extensive use of digital cameras for color imaging. This procedure has a much cheaper process than the use of traditional contact-type color measurement devices. Digital cameras can be taken by the place of contact-type instruments for shade selection and overcome their disadvantages. Images taken from teeth show morphology and color texture of teeth. In last decades, a new method was recommended to compare the color of shade tabs taken by a digital camera using color features. This method showed that visual and computer-aided shade matching systems should be used as concatenated. Recently using methods of feature extraction techniques are based on shape description and not used color information. However, color is mostly experienced as an essential property in depicting and extracting features from objects in the world around us. When local feature descriptors with color information are extended by concatenating color descriptor with the shape descriptor, that descriptor will be effective on visual object recognition and classification task. Therefore, the color descriptor is to be used in combination with a shape descriptor it does not need to contain any spatial information, which leads us to use local histograms. This local color histogram method is remain reliable under variation of photometric changes, geometrical changes and variation of image quality. So, coloring local feature extraction methods are used to extract features, and also the Scale Invariant Feature Transform (SIFT) descriptor used to for shape description in the proposed method. After the combination of these descriptors, the state-of-art descriptor named by Color-SIFT will be used in this study. Finally, the image feature vectors obtained from quantization algorithm are fed to classifiers such as Nearest Neighbor (KNN), Naive Bayes or Support Vector Machines (SVM) to determine label(s) of the visual object category or matching. In this study, SVM are used as classifiers for color determination and shade matching. Finally, experimental results of this method will be compared with other recent studies. It is concluded from the study that the proposed method is remarkable development on computer aided tooth shade determination system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifiers" title="classifiers">classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20determination" title=" color determination"> color determination</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20system" title=" computer-aided system"> computer-aided system</a>, <a href="https://publications.waset.org/abstracts/search?q=tooth%20shade%20matching" title=" tooth shade matching"> tooth shade matching</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/51113/a-computer-aided-system-for-tooth-shade-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51113.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">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1596</span> A New 3D Shape Descriptor Based on Multi-Resolution and Multi-Block CS-LBP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nihad%20Karim%20Chowdhury">Nihad Karim Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sanaullah%20Chowdhury"> Mohammad Sanaullah Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammed%20Jamshed%20Alam%20Patwary"> Muhammed Jamshed Alam Patwary</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubel%20Biswas"> Rubel Biswas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20shape%20retrieval" title="3D shape retrieval">3D shape retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20shape%20descriptor" title=" 3D shape descriptor"> 3D shape descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=CS-LBP" title=" CS-LBP"> CS-LBP</a>, <a href="https://publications.waset.org/abstracts/search?q=overlapped%20sub-images" title=" overlapped sub-images"> overlapped sub-images</a> </p> <a href="https://publications.waset.org/abstracts/40165/a-new-3d-shape-descriptor-based-on-multi-resolution-and-multi-block-cs-lbp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40165.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">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1595</span> 3D Objects Indexing Using Spherical Harmonic for Optimum Measurement Similarity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Hellam">S. Hellam</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Oulahrir"> Y. Oulahrir</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20El%20Mounchid"> F. El Mounchid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sadiq"> A. Sadiq</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mbarki"> S. Mbarki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a method for three-dimensional (3-D)-model indexing based on defining a new descriptor, which we call new descriptor using spherical harmonics. The purpose of the method is to minimize, the processing time on the database of objects models and the searching time of similar objects to request object. Firstly we start by defining the new descriptor using a new division of 3-D object in a sphere. Then we define a new distance which will be used in the search for similar objects in the database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20indexation" title="3D indexation">3D indexation</a>, <a href="https://publications.waset.org/abstracts/search?q=spherical%20harmonic" title=" spherical harmonic"> spherical harmonic</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20of%203D%20objects" title=" similarity of 3D objects"> similarity of 3D objects</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20similarity" title=" measurement similarity"> measurement similarity</a> </p> <a href="https://publications.waset.org/abstracts/14277/3d-objects-indexing-using-spherical-harmonic-for-optimum-measurement-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14277.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">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1594</span> Enhanced Face Recognition with Daisy Descriptors Using 1BT Based Registration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sevil%20Igit">Sevil Igit</a>, <a href="https://publications.waset.org/abstracts/search?q=Merve%20Meric"> Merve Meric</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarp%20Erturk"> Sarp Erturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20descriptor" title=" Daisy descriptor"> Daisy descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=One-Bit%20Transform" title=" One-Bit Transform"> One-Bit Transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20registration" title=" image registration"> image registration</a> </p> <a href="https://publications.waset.org/abstracts/12593/enhanced-face-recognition-with-daisy-descriptors-using-1bt-based-registration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12593.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">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1593</span> Efficient Model Order Reduction of Descriptor Systems Using Iterative Rational Krylov Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Anwar">Muhammad Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameen%20Ullah"> Ameen Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Intakhab%20Alam%20Qadri"> Intakhab Alam Qadri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a technique utilizing the Iterative Rational Krylov Algorithm (IRKA) to reduce the order of large-scale descriptor systems. Descriptor systems, which incorporate differential and algebraic components, pose unique challenges in Model Order Reduction (MOR). The proposed method partitions the descriptor system into polynomial and strictly proper parts to minimize approximation errors, applying IRKA exclusively to the strictly adequate component. This approach circumvents the unbounded errors that arise when IRKA is directly applied to the entire system. A comparative analysis demonstrates the high accuracy of the reduced model and a significant reduction in computational burden. The reduced model enables more efficient simulations and streamlined controller designs. The study highlights IRKA-based MOR’s effectiveness in optimizing complex systems’ performance across various engineering applications. The proposed methodology offers a promising solution for reducing the complexity of large-scale descriptor systems while maintaining their essential characteristics and facilitating their analysis, simulation, and control design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20order%20reduction" title="model order reduction">model order reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptor%20systems" title=" descriptor systems"> descriptor systems</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20rational%20Krylov%20algorithm" title=" iterative rational Krylov algorithm"> iterative rational Krylov algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=interpolatory%20model%20reduction" title=" interpolatory model reduction"> interpolatory model reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20efficiency" title=" computational efficiency"> computational efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20methods" title=" projection methods"> projection methods</a>, <a href="https://publications.waset.org/abstracts/search?q=H%E2%82%82-optimal%20model%20reduction" title=" H₂-optimal model reduction"> H₂-optimal model reduction</a> </p> <a href="https://publications.waset.org/abstracts/189198/efficient-model-order-reduction-of-descriptor-systems-using-iterative-rational-krylov-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189198.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">31</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1592</span> Implementation of a Multimodal Biometrics Recognition System with Combined Palm Print and Iris Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabab%20M.%20Ramadan">Rabab M. Ramadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaraby%20A.%20Elgallad"> Elaraby A. Elgallad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title="iris recognition">iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=palm%20print" title=" palm print"> palm print</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20Scale%20Invariant%20Feature%20Transform%20%28SIFT%29" title=" the Scale Invariant Feature Transform (SIFT)"> the Scale Invariant Feature Transform (SIFT)</a> </p> <a href="https://publications.waset.org/abstracts/90535/implementation-of-a-multimodal-biometrics-recognition-system-with-combined-palm-print-and-iris-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90535.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">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1591</span> Hand Motion Trajectory Analysis for Dynamic Hand Gestures Used in Indian Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daleesha%20M.%20Viswanathan">Daleesha M. Viswanathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumam%20Mary%20Idicula"> Sumam Mary Idicula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=orientation%20features" title="orientation features">orientation features</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20feature%20vector" title=" discrete feature vector"> discrete feature vector</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM." title=" HMM."> HMM.</a>, <a href="https://publications.waset.org/abstracts/search?q=Indian%20sign%20language" title=" Indian sign language"> Indian sign language</a> </p> <a href="https://publications.waset.org/abstracts/35653/hand-motion-trajectory-analysis-for-dynamic-hand-gestures-used-in-indian-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35653.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">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1590</span> A Biologically Inspired Approach to Automatic Classification of Textile Fabric Prints Based On Both Texture and Colour Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babar%20Khan">Babar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhijie"> Wang Zhijie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20classification" title="automatic classification">automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=colour%20descriptor" title=" colour descriptor"> colour descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=opponent%20colour%20channel" title=" opponent colour channel"> opponent colour channel</a> </p> <a href="https://publications.waset.org/abstracts/31715/a-biologically-inspired-approach-to-automatic-classification-of-textile-fabric-prints-based-on-both-texture-and-colour-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31715.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">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1589</span> Human Action Retrieval System Using Features Weight Updating Based Relevance Feedback Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Munaf%20Rashid">Munaf Rashid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=relevance%20feedback%20%28RF%29" title="relevance feedback (RF)">relevance feedback (RF)</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20retrieval" title=" action retrieval"> action retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20gap" title=" semantic gap"> semantic gap</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20descriptor" title=" feature descriptor"> feature descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=codebook" title=" codebook"> codebook</a> </p> <a href="https://publications.waset.org/abstracts/41740/human-action-retrieval-system-using-features-weight-updating-based-relevance-feedback-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41740.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">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1588</span> Global Based Histogram for 3D Object Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somar%20Boubou">Somar Boubou</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatsuo%20Narikiyo"> Tatsuo Narikiyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Michihiro%20Kawanishi"> Michihiro Kawanishi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vision%20in%20control" title="vision in control">vision in control</a>, <a href="https://publications.waset.org/abstracts/search?q=robotics" title=" robotics"> robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20histogram%20of%20normal%20vectors" title=" differential histogram of normal vectors"> differential histogram of normal vectors</a> </p> <a href="https://publications.waset.org/abstracts/47486/global-based-histogram-for-3d-object-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47486.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">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1587</span> Emotion Recognition with Occlusions Based on Facial Expression Reconstruction and Weber Local Descriptor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jadisha%20Cornejo">Jadisha Cornejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Helio%20Pedrini"> Helio Pedrini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20expression" title=" facial expression"> facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=occlusion" title=" occlusion"> occlusion</a>, <a href="https://publications.waset.org/abstracts/search?q=fiducial%20landmarks" title=" fiducial landmarks"> fiducial landmarks</a> </p> <a href="https://publications.waset.org/abstracts/90510/emotion-recognition-with-occlusions-based-on-facial-expression-reconstruction-and-weber-local-descriptor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90510.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">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1586</span> Efficient Human Motion Detection Feature Set by Using Local Phase Quantization Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arwa%20Alzughaibi">Arwa Alzughaibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20detection" title="human motion detection">human motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=histograms%20of%20oriented%20gradient" title=" histograms of oriented gradient"> histograms of oriented gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a> </p> <a href="https://publications.waset.org/abstracts/48160/efficient-human-motion-detection-feature-set-by-using-local-phase-quantization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48160.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">258</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1585</span> Classifying Facial Expressions Based on a Motion Local Appearance Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabiola%20M.%20Villalobos-Castaldi">Fabiola M. Villalobos-Castaldi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicol%C3%A1s%20C.%20Kemper"> Nicolás C. Kemper</a>, <a href="https://publications.waset.org/abstracts/search?q=Esther%20Rojas-Krugger"> Esther Rojas-Krugger</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20G.%20Ram%C3%ADrez-S%C3%A1nchez"> Laura G. Ramírez-Sánchez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the classification results about exploring the combination of a motion based approach with a local appearance method to describe the facial motion caused by the muscle contractions and expansions that are presented in facial expressions. The proposed feature extraction method take advantage of the knowledge related to which parts of the face reflects the highest deformations, so we selected 4 specific facial regions at which the appearance descriptor were applied. The most common used approaches for feature extraction are the holistic and the local strategies. In this work we present the results of using a local appearance approach estimating the correlation coefficient to the 4 corresponding landmark-localized facial templates of the expression face related to the neutral face. The results let us to probe how the proposed motion estimation scheme based on the local appearance correlation computation can simply and intuitively measure the motion parameters for some of the most relevant facial regions and how these parameters can be used to recognize facial expressions automatically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facial%20expression%20recognition%20system" title="facial expression recognition system">facial expression recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=local-appearance%20method" title=" local-appearance method"> local-appearance method</a>, <a href="https://publications.waset.org/abstracts/search?q=motion-based%20approach" title=" motion-based approach"> motion-based approach</a> </p> <a href="https://publications.waset.org/abstracts/27632/classifying-facial-expressions-based-on-a-motion-local-appearance-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27632.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">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1584</span> Model Order Reduction of Continuous LTI Large Descriptor System Using LRCF-ADI and Square Root Balanced Truncation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sahadet%20Hossain">Mohammad Sahadet Hossain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamsil%20Arifeen"> Shamsil Arifeen</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrab%20Hossian%20Likhon"> Mehrab Hossian Likhon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze a linear time invariant (LTI) descriptor system of large dimension. Since these systems are difficult to simulate, compute and store, we attempt to reduce this large system using Low Rank Cholesky Factorized Alternating Directions Implicit (LRCF-ADI) iteration followed by Square Root Balanced Truncation. LRCF-ADI solves the dual Lyapunov equations of the large system and gives low-rank Cholesky factors of the gramians as the solution. Using these cholesky factors, we compute the Hankel singular values via singular value decomposition. Later, implementing square root balanced truncation, the reduced system is obtained. The bode plots of original and lower order systems are used to show that the magnitude and phase responses are same for both the systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low-rank%20cholesky%20factor%20alternating%20directions%20implicit%20iteration" title="low-rank cholesky factor alternating directions implicit iteration">low-rank cholesky factor alternating directions implicit iteration</a>, <a href="https://publications.waset.org/abstracts/search?q=LTI%20Descriptor%20system" title=" LTI Descriptor system"> LTI Descriptor system</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20equations" title=" Lyapunov equations"> Lyapunov equations</a>, <a href="https://publications.waset.org/abstracts/search?q=Square-root%20balanced%20truncation" title=" Square-root balanced truncation"> Square-root balanced truncation</a> </p> <a href="https://publications.waset.org/abstracts/26947/model-order-reduction-of-continuous-lti-large-descriptor-system-using-lrcf-adi-and-square-root-balanced-truncation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26947.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">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1583</span> AS-Geo: Arbitrary-Sized Image Geolocalization with Learnable Geometric Enhancement Resizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huayuan%20Lu">Huayuan Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chunfang%20Yang"> Chunfang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma%20Zhu"> Ma Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Baojun%20Qi"> Baojun Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaqiong%20Qiao"> Yaqiong Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangqian%20Xu"> Jiangqian Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20geolocalization" title="image geolocalization">image geolocalization</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention%20mechanism" title=" self-attention mechanism"> self-attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20resizer" title=" image resizer"> image resizer</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20feature" title=" geometric feature"> geometric feature</a> </p> <a href="https://publications.waset.org/abstracts/152265/as-geo-arbitrary-sized-image-geolocalization-with-learnable-geometric-enhancement-resizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152265.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">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1582</span> Evaluation of Robust Feature Descriptors for Texture Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia-Hong%20Lee">Jia-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei-Yi%20Wu"> Mei-Yi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsung%20Kuo"> Hsien-Tsung Kuo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20classification" title="texture classification">texture classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF" title=" SURF"> SURF</a>, <a href="https://publications.waset.org/abstracts/search?q=ORB" title=" ORB"> ORB</a> </p> <a href="https://publications.waset.org/abstracts/11046/evaluation-of-robust-feature-descriptors-for-texture-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11046.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">369</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1581</span> A New Method Separating Relevant Features from Irrelevant Ones Using Fuzzy and OWA Operator Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imed%20Feki">Imed Feki</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Msahli"> Faouzi Msahli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20sensitivity" title="data sensitivity">data sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=OWA%20operators" title=" OWA operators"> OWA operators</a>, <a href="https://publications.waset.org/abstracts/search?q=percolation%20technique" title=" percolation technique"> percolation technique</a> </p> <a href="https://publications.waset.org/abstracts/27691/a-new-method-separating-relevant-features-from-irrelevant-ones-using-fuzzy-and-owa-operator-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27691.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">605</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1580</span> The Effect of Feature Selection on Pattern Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Fong%20Tsai">Chih-Fong Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya-Han%20Hu"> Ya-Han Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/5047/the-effect-of-feature-selection-on-pattern-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5047.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">669</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1579</span> A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samina%20Khalid">Samina Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamila%20Nasreen"> Shamila Nasreen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=age%20related%20macular%20degeneration" title="age related macular degeneration">age related macular degeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20feature%20subset%20selection%20feature%20extraction%2Ftransformation" title=" feature selection feature subset selection feature extraction/transformation"> feature selection feature subset selection feature extraction/transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=FSA%E2%80%99s" title=" FSA’s"> FSA’s</a>, <a href="https://publications.waset.org/abstracts/search?q=relief" title=" relief"> relief</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20based%20method" title=" correlation based method"> correlation based method</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a> </p> <a href="https://publications.waset.org/abstracts/6168/a-survey-of-feature-selection-and-feature-extraction-techniques-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6168.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">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1578</span> Local Directional Encoded Derivative Binary Pattern Based Coral Image Classification Using Weighted Distance Gray Wolf Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annalakshmi%20G.">Annalakshmi G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthivel%20Murugan%20S."> Sakthivel Murugan S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20directional%20pattern" title=" local directional pattern"> local directional pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=ELM%20classifier" title=" ELM classifier"> ELM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=GWO%20optimization" title=" GWO optimization"> GWO optimization</a> </p> <a href="https://publications.waset.org/abstracts/142439/local-directional-encoded-derivative-binary-pattern-based-coral-image-classification-using-weighted-distance-gray-wolf-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142439.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">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1577</span> Hybrid Feature Selection Method for Sentiment Classification of Movie Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Goyal">Vishnu Goyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Basant%20Agarwal"> Basant Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis research provides methods for identifying the people’s opinion written in blogs, reviews, social networking websites etc. Sentiment analysis is to understand what opinion people have about any given entity, object or thing. Sentiment analysis research can be broadly categorised into three types of approaches i.e. semantic orientation, machine learning and lexicon based approaches. Feature selection methods improve the performance of the machine learning algorithms by eliminating the irrelevant features. Information gain feature selection method has been considered best method for sentiment analysis; however, it has the drawback of selection of threshold. Therefore, in this paper, we propose a hybrid feature selection methods comprising of information gain and proposed feature selection method. Initially, features are selected using Information Gain (IG) and further more noisy features are eliminated using the proposed feature selection method. Experimental results show the efficiency of the proposed feature selection methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20feature%20selection" title=" hybrid feature selection"> hybrid feature selection</a> </p> <a href="https://publications.waset.org/abstracts/59737/hybrid-feature-selection-method-for-sentiment-classification-of-movie-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59737.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">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1576</span> Feature Location Restoration for Under-Sampled Photoplethysmogram Using Spline Interpolation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hangsik%20Shin">Hangsik Shin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research is to restore the feature location of under-sampled photoplethysmogram using spline interpolation and to investigate feasibility for feature shape restoration. We obtained 10 kHz-sampled photoplethysmogram and decimated it to generate under-sampled dataset. Decimated dataset has 5 kHz, 2.5 k Hz, 1 kHz, 500 Hz, 250 Hz, 25 Hz and 10 Hz sampling frequency. To investigate the restoration performance, we interpolated under-sampled signals with 10 kHz, then compared feature locations with feature locations of 10 kHz sampled photoplethysmogram. Features were upper and lower peak of photplethysmography waveform. Result showed that time differences were dramatically decreased by interpolation. Location error was lesser than 1 ms in both feature types. In 10 Hz sampled cases, location error was also deceased a lot, however, they were still over 10 ms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=peak%20detection" title="peak detection">peak detection</a>, <a href="https://publications.waset.org/abstracts/search?q=photoplethysmography" title=" photoplethysmography"> photoplethysmography</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling" title=" sampling"> sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20reconstruction" title=" signal reconstruction"> signal reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/53409/feature-location-restoration-for-under-sampled-photoplethysmogram-using-spline-interpolation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53409.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">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1575</span> Classification of Political Affiliations by Reduced Number of Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vesile%20Evrim">Vesile Evrim</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20Awwal"> Aliyu Awwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=LIWC" title=" LIWC"> LIWC</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=politics" title=" politics"> politics</a> </p> <a href="https://publications.waset.org/abstracts/27684/classification-of-political-affiliations-by-reduced-number-of-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27684.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">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1574</span> Processing Big Data: An Approach Using Feature Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikat%20Parveen">Nikat Parveen</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ananthi"> M. Ananthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20value" title=" key value"> key value</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/74596/processing-big-data-an-approach-using-feature-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74596.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">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1573</span> Relevance Feedback within CBIR Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mawloud%20Mosbah">Mawloud Mosbah</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Boucheham"> Bachir Boucheham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-Nearest Neighbours Algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing colour moments on the RGB space. This compact descriptor, Colour Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CBIR" title="CBIR">CBIR</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20search" title=" category search"> category search</a>, <a href="https://publications.waset.org/abstracts/search?q=relevance%20feedback" title=" relevance feedback"> relevance feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20point%20movement" title=" query point movement"> query point movement</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20Rocchio%E2%80%99s%20formula" title=" standard Rocchio’s formula"> standard Rocchio’s formula</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20shifting%20query" title=" adaptive shifting query"> adaptive shifting query</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20weighting" title=" feature weighting"> feature weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=original%20KNN" title=" original KNN"> original KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20KNN" title=" incremental KNN"> incremental KNN</a> </p> <a href="https://publications.waset.org/abstracts/7872/relevance-feedback-within-cbir-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7872.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">280</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" 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