Color Texture Classification Using Rao Distance between Multivariate Copula Based Models

Overview & Synopsis

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.