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.
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WIAMIS '08: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, May 2008, Klagenfurt, Austria. IEEE Computer Society, pp.28--31, 2008, 〈10.1109/WIAMIS.2008.46〉
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Sandrine Anthoine, Eric Debreuve, Paolo Piro, Michel Barlaud. Using Neighborhood Distributions of Wavelet Coefficients for On-the-Fly, Multiscale-Based Image Retrieval. WIAMIS '08: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, May 2008, Klagenfurt, Austria. IEEE Computer Society, pp.28--31, 2008, 〈10.1109/WIAMIS.2008.46〉. 〈hal-00382777〉

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