Image retrieval via Kullback-Leibler divergence of patches of multiscale coefficients in the KNN framework

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.