Laguerre Gauss analysis for image retrieval based on color texture

Abstract

In this work a novel technique for color texture representations and classifications is presented. We assume that a color texture can be mainly characterized by two components: structure and color. Concerning the structure, it is analyzed by using the Laguerre-Gauss circular harmonic wavelet decomposition of the luminance channel. At this aim, the marginal density of the wavelet coefficients is modeled by Generalized Gaussian Density (GGD), and the similarity is based on the Kullback-Leibler divergence (KLD) between two GGDs. The color is characterized by the moments computed on the chromatic channels, and the similarity is evaluated by using the Euclidean distance. The overall similarity is obtained by linearly combining the two individual measures. Experimental results on a data set of 640 color texture images, extracted from the "Vision Texture" database, show that the retrieval rates is about 81% when only the structural component is employed, and it rises up to 87% when using both structural and color components.