Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy–Schwarz divergence in wavelet domain |
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| Affiliation: | 1. LRIT URAC 29, Faculty of Sciences, Mohammed V University in Rabat, Morocco;2. CRAN, CNRS UMR 7039, University of Nancy, France;3. DESTEC, FLSHR, Mohammed V University in Rabat, Morocco;4. L.I.M. Faculty of Sciences Dhar el Mahraz, USMBA, Fès, Morocco;1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China;2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China;1. School of Electronics and Information, Northwestern Polytechnical University, Xi?an, China;2. School of Electrical and Computer Engineering, Royal Melbourne Institute of Technology, Melbourne, Victoria 3001, Australia;3. State Key Laboratory of ISN Xidian University, Xi?an, China;1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China;2. School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003, China;1. Universidade Federal de Pernambuco, Departamento de Estatística, Brazil;2. Universidade Federal do Ceará, Departamento de Estatística e Matemática Aplicada, Brazil;3. ECE, University of Calgary, Canada |
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| Abstract: | This paper presents a novel similarity measure in a texture retrieval framework based on statistical modeling in wavelet domain. In this context, we use the recently proposed finite mixture of generalized Gaussian distribution (MoGG) thanks to its ability to model accurately a wide range of wavelet sub-bands histograms. This model has already been relied on the approximation of Kullback–Leibler divergence (KLD) which hinders significantly the retrieval process. To overcome this drawback, we introduce the Cauchy–Schwarz divergence (CSD) between two MoGG distributions as a similarity measure. Hence, an analytic closed-form expression of this measure is developed in the case of fixed shape parameter. Otherwise, when the shape parameter is variable, two approximations are derived using the well-known stochastic integration with Monte-Carlo simulations and numerical integration with Simpson?s rule. Experiments conducted on a well known dataset show good performance of the CSD in terms of retrieval rates and the computational time improvement compared to the KLD. |
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| Keywords: | Wavelet decomposition Mixture of generalized Gaussian model Similarity measurement Cauchy–Schwarz divergence |
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