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

Sandrine Anthoine (1) , Eric Debreuve (1) , Paolo Piro (1) , Michel Barlaud (1)

1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe IMAGES-CREATIVE

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.

Mots clés: sparse wavelet description; intra/interscale dependency; Kullback-Leibler divergence; k-th nearest neighbors; Image retrieval

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

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. pp.28--31, ⟨10.1109/WIAMIS.2008.46⟩. ⟨hal-00382777⟩