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We propose a novel statistical approach for color texture modeling and classification based on cooccurrence matrices and discrete finite mixture models. Our statistical model assigns relevance weights to discrete cooccurrence features that are considered as random variables. Experimental results are presented to illustrate the merits of our approach on a difficult problem which is the categorization...
Many computer vision and pattern recognition problems involve the use of finite Gaussian mixture models. Finite mixture model using generalized Dirichlet distribution has been shown as a robust alternative of normal mixtures. In this paper, we adopt a Bayesian approach for generalized Dirichlet mixture estimation and selection. This approach, offers a solid theoretical framework for combining both...
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