Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models

This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which, in contrast to the Kullback-Leibler divergence, exists in a closed form for any fixed value of the shape pa rameter of the distribution. We apply this in several retrieval experiments. The modeling of the interband correlation significantly increases retrieval rates, while the geodesic distance is shown to outperform the Kullback- Leibler divergence. A multivariate Laplace distribution yields better results than a Gaussian, indicating the potential of a model with variable shape parameter together with the geodesic distance.