{"title":"Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping","authors":"Adnan A. Y. Mustafa","volume":137,"journal":"International Journal of Computer and Information Engineering","pagesStart":310,"pagesEnd":315,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10009008","abstract":"<p>In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.<\/p>\r\n","references":"[1]\tA. A. Mustafa, \u201cProbabilistic Model for Quick Detection of Dissimilar Binary Images\u201d. Journal of Electronic Imaging, 24, 5, 2015, pp. 24-53.\r\n[2]\tA. A. Mustafa, \u201cA Probabilistic Model for Random Binary Image Mapping\u201d. WSEAS Transactions on Systems and Control, Volume 12, 2017, Art. #34, pp. 317-331. \r\n[3]\tP. Jaccard, \u201cDistribution de la flore alpine dans le bassin des Dranses et dans quelques r\u00e9gions voisines\u201d. Bulletin de la Soci\u00e9t\u00e9 Vaudoise des Sciences Naturelles, 1901, 37, pp. 241-272.\r\n[4]\tR. Sokal and C. Michener, \u201cA statistical method for evaluating systematic relationships\u201d, Bulletin of the Society of University of Kansas, 1958, 38, pp. 1409-1438.\r\n[5]\tR. Hamming, \u201cError detecting and error correcting codes\u201d. Bell System Technical Journal, 1950, 29, (2), pp. 147\u2013160.\r\n[6]\tG. Sidorov et al., \u201cSoft similarity and soft cosine measure: Similarity of features in vector space model\u201d. Computaci\u00f3n y Sistemas, 2014, 18, (3), pp. 491-504. \r\n[7]\tP. Anuta, \u201cSpatial Registration of Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform Techniques\u201d. IEEE Transactions on Geoscience Electronics, GE-8, N 4, 1970, pp. 353-368.\r\n[8]\tD. Barnea and H. Silverman, \u201cA Class of Algorithms for Fast Digital Image Registration\u201d. IEEE Trans. on Computers, Vol. c-21, N 2, 1972, pp.179-186.\r\n[9]\tJ. Pluim, A. Maintz and M. Viergever, \u201cMutual-Information-Based Registration of Medical Images: A Survey\u201d. IEEE Transactions on Medical Imaging, 22, 8, 2003.\r\n[10]\tA. A. Mustafa, \u201cA Modified Hamming Distance Measure for Quick Rejection of Dissimilar Binary Images\u201d. International Conference on Computer Vision and Image Analysis, 2015.\r\n[11]\tS. Choi, S. Cha, and C. Tappert, \u2018A Survey of Binary Similarity and Distance Measures\u2019. Journal of Systems, Cybernetics and Informatics, 2010, 8, (1), pp. 43-48.\r\n[12]\tA. A. Mustafa, \u201cQuick Probabilistic Binary Image Matching: Changing the Rules of the Game\u201d. Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997112 (September 27, 2016); doi:10.1117\/12.2237552.\r\n[13]\tA. A. Mustafa, \u201cA Probabilistic Binary Similarity Distance for Quick Image Matching\u201d. IET Journal on Image Processing, submitted for review.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 137, 2018"}