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.