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Communication Dans Un Congrès Année : 2008

Image Retrieval via Kullback-Leibler Divergence of Patches of Multiscale Coefficients in the KNN Framework

Résumé

In this paper, we define a similarity measure between images in the context of (indexing and) retrieval. We use the Kullback-Leibler (KL) divergence to compare sparse multiscale image representations. The KL divergence between parameterized marginal distributions of wavelet coefficients has already been used as a similarity measure between images. Here we use the Laplacian pyramid and consider the dependencies between coefficients by means of nonparametric distributions of mixed intra/interscale and interchannel patches. To cope with the high-dimensionality of the resulting description space, we estimate the KL divergences in the k-th nearest neighbor (kNN) framework (instead of classical fixed size kernel methods). Query-by-example experiments show the accuracy and robustness of the method.
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Dates et versions

hal-00382780 , version 1 (11-05-2009)

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Citer

Paolo Piro, Sandrine Anthoine, Eric Debreuve, Michel Barlaud. Image Retrieval via Kullback-Leibler Divergence of Patches of Multiscale Coefficients in the KNN Framework. CBMI 2008. Proceedings of the International Workshop on Content-Based Multimedia Indexing, Jun 2008, London, United Kingdom. pp.230 - 235, ⟨10.1109/CBMI.2008.4564951⟩. ⟨hal-00382780⟩
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