Color Texture Classification Using Rao Distance between Multivariate Copula Based Models

Abstract : This paper presents a new similarity measure based on Rao distance for color texture classi cation or retrieval. Textures are charac-terized by a joint model of complex wavelet coe cients. This model is based on a Gaussian Copula in order to consider the dependency between color components. Then, a closed form of Rao distance is computed to measure the di erence between two Gaussian Copula based probability density functions on the corresponding manifold. Results in term of clas-si cation rates, show the e ectiveness of the Rao geodesic distance when applied on the manifold of Gaussian Copula based probability distribu-tions, in comparison with the Kullback-Leibler divergence.
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Pedro Real and Daniel Diaz-Pernil and Helena Molina-Abril and Ainhoa Berciano and Walter Kropatsch. Computer Analysis of Images and Patterns, Springer Berlin Heidelberg, pp.498-505, 2011, 978-3-642-23677-8. 〈10.1007/978-3-642-23678-5〉
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Ahmed Drissi El Maliani, Mohammed El Hassouni, Nour-Eddine Lasmar, Yannick Berthoumieu, Driss Aboutajdine. Color Texture Classification Using Rao Distance between Multivariate Copula Based Models. Pedro Real and Daniel Diaz-Pernil and Helena Molina-Abril and Ainhoa Berciano and Walter Kropatsch. Computer Analysis of Images and Patterns, Springer Berlin Heidelberg, pp.498-505, 2011, 978-3-642-23677-8. 〈10.1007/978-3-642-23678-5〉. 〈hal-00727122〉

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