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Copulas based multivariate Gamma modeling for texture classification

Abstract : This paper deals with texture modeling for classification or retrieval systems using multivariate statistical features. The proposed features are defined by the hyperparameters of a copula-based multivariate distribution characterizing the coefficients provided by image decomposition in scale and orientation. As it belongs to the multivariate stochastic models, the copulas are useful to describe pairwise non-linear association in the case of multivariate non-Gaussian density. In this paper, we propose the d-variate Gaussian copula associated to univariate Gamma densities for modeling the texture. Experiments were conducted on the VisTex database aiming to compare the recognition rates of the proposed model with the univariate generalized Gaussian model, the univariate Gamma model, and the generalized Gaussian copula-based multivariate model.
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Contributor : Nour-Eddine Lasmar Connect in order to contact the contributor
Submitted on : Tuesday, September 4, 2012 - 12:27:50 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM
Long-term archiving on: : Wednesday, December 5, 2012 - 10:04:53 AM


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Youssef Stitou, Nour-Eddine Lasmar, Yannick Berthoumieu. Copulas based multivariate Gamma modeling for texture classification. IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, Taipei, Taiwan. pp.1045-1048, ⟨10.1109/ICASSP.2009.4959766⟩. ⟨hal-00727108⟩



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