Fast scalable retrieval of multispectral images with Kullback-Leibler divergence

In this paper, we are interested in multicomponent image indexing in the Wavelet Transform (WT) domain. In this respect, the joint distribution of the WT coefficients through all the channels is modeled by a parametric copula-based model. The parameters of this model are considered as the salient signatures of the image content. The relevance of this model is based on a reliable choice of both the appropriate marginal distributions and the copula density reflecting the cross-component correlation. The similarity measure is chosen as the Kullback-Leibler divergence. The contribution of this work consists in proposing an organization of the features database in order to enable a coarse-to-fine resolution retrieval procedure suitable for progressive telebrowsing applications. Experimental results indicate that our new approach drastically reduces the retrieval time while maintaining acceptable retrieval performances.