Using Neighborhood Distributions of Wavelet Coefficients for On-the-Fly, Multiscale-Based Image Retrieval

Abstract

In this paper, we define a similarity measure to compare images in the context of (indexing and) retrieval. We use the Kullback-Leibler (KL) divergence to compare sparse multiscale image descriptions in a wavelet domain. The KL divergence between wavelet coefficient distributions has already been used as a similarity measure between images. The novelty here is twofold. Firstly, we consider the dependencies between the coefficients by means of distributions of mixed intra/interscale neighborhoods. Secondly, to cope with the high-dimensionality of the resulting description space, we estimate the KL divergences in the k-th nearest neighbor framework, instead of using classical fixed size kernel methods. Query-by-example experiments are presented.

DOI: 10.1109/WIAMIS.2008.46

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@article{Anthoine2008UsingND, title={Using Neighborhood Distributions of Wavelet Coefficients for On-the-Fly, Multiscale-Based Image Retrieval}, author={Sandrine Anthoine and Eric Debreuve and Paolo Piro and Michel Barlaud}, journal={2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services}, year={2008}, pages={28-31} }