Color texture classification method based on a statistical multi-model and geodesic distance

•A statistical multi-model for color texture classification is proposed.•We use perceptual color spaces HSV and Lab as an alternative of RGB.•We use the copula theory to build our models for luminance and chrominance.•We derive a closed-form of geodesic distance between two copulas.•We use the Bayesian classifier to assess the performance of our multi-model.

In this paper, we propose a novel color texture classification method based on statistical characterization. The approach consists in modeling complex wavelet coefficients of both luminance and chrominance components separately leading to a multi-modeling approach. The copula theory allows to take into account the spatial dependencies which exist within the intra-luminance sub-bands via the luminance model MLML, and also between the inter-chrominance subband coefficients via the chrominance model MCrMCr. The multi-model, i.e. MLML and MCrMCr, is used to develop a Bayesian classifier based on the softmax principal. To derive the classifier, we propose a closed-form expression for the Rao geodesic distance between two copulas. Experiments on two sub-families of luminance-chrominance color spaces namely Lab and HSV have been carried out for a wide range of color texture databases. The combination of different statistical sub-models show that the multi-modeling performs better than some existing methods in term of classification rates.