Multivariate generalized gamma distribution for content based image retrieval

被引：0

作者：

El Maliani, Ahmed Drissi ^{[1
]}

El Hassouni, Mohammed ^{[2
]}

Berthoumieu, Yannick ^{[3
]}

Aboutajdine, Driss ^{[1
]}

机构：

[1] LRIT, Unitée Associée au CNRST (URAC 29), Mohammed V University, Agdal

[2] DESTEC, FLSHR, Mohammed V University, Agdal

[3] Univ. Bordeaux, IPB, IMS, Groupe Signal UMR 5218

来源：

Journal of Convergence Information Technology | 2012年 / 7卷 / 20期

关键词：

Copulas; Kullback-leibler divergence; Texture retrieval;

D O I：

10.4156/jcit.vol7.issue20.38

中图分类号：

学科分类号：

摘要：

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 (MGΓD) 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 twoMGΓDs. Performances of the CBIR system show the superiority of the proposed model over a variety of multivariate models.

引用

页码：319 / 327

页数：8

共 29 条

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