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

Authors:
Sarra Sakji-Nsibi;Amel Benazza-Benyahia
Affiliations:
Unité de Recherche en Imagerie Satellitaire, Ecole Supérieure des Communications de Tunis, Cité des Communications, Ariana, Tunisia;Unité de Recherche en Imagerie Satellitaire, Ecole Supérieure des Communications de Tunis, Cité des Communications, Ariana, Tunisia
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

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.