Multivariate Generalized Gamma Distribution for Content Based Image Retrieval
نویسندگان
چکیده
This paper deals with the joint modeling of color textures in the context of Content Based Image Retrieval (CBIR). We propose a generic multivariate model based on the Generalized Gamma distribution to describe the marginal behavior of texture wavelet detail subbands. Then the information of dependence across color components is incorporated in the modeling process using the Gaussian copula. The multivariate Generalized Gamma distribution ( D MG ) is advantageous in term of flexibility when compared with other joint models. As similarity measure, we propose a closed-form of the Kullback-leibler (KL) divergence between two Ds MG . Performances of the CBIR system show the superiority of the proposed model over a variety of multivariate models.
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