Copula-based statistical models for multicomponent image retrieval in thewavelet transform domain

In this paper, we are interested in multicomponent image indexing in the wavelet transform (WT) domain. More precisely, a WT is applied to each component then a suitable parametric model is retained for the distribution model of the wavelet coefficients. The parameters of this model are chosen as the salient features of the image content. The contribution of this work consists in choosing a parametric model which reflects the main dependencies existing between the resulting coefficients consisting of cross-component correlations and inter-scale similarities. The copula concept is introduced for building an appropriate statistical model of all the wavelet coefficients. Once the signatures are extracted, the retrieval procedure associated with a given query image is performed. Experimental results indicate that considering simultaneously the cross-component and the inter-scale correlation drastically improves the retrieval performances of the wavelet-based retrieval system.