Skip to Main content Skip to Navigation
Book sections

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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Nour-Eddine Lasmar Connect in order to contact the contributor
Submitted on : Sunday, September 2, 2012 - 10:43:23 AM
Last modification on : Friday, August 9, 2019 - 3:10:32 PM
Long-term archiving on: : Monday, December 3, 2012 - 2:35:07 AM


Files produced by the author(s)



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⟩



Les métriques sont temporairement indisponibles