This book details the conception, development and analysis of a novel color texture descriptor based on the luminance-chrominance complex linear prediction models for perceptual color spaces. The accuracy and precision of the estimated spectral densities along with the spectral distance measures ensure the robustness and pertinence of the approach for color texture classification. These results have shown that the chrominance structure information of the color textured images could get better characterized in L*a*b* color space and hence could provide the better color texture classification results. A Bayesian framework based on the multichannel linear prediction error is also developed for the segmentation of textured color images. The main contribution of this segmentation methodology resides in the robust parametric approximations proposed for the multichannel linear prediction error distribution. Experimental results for the segmentation of synthetic color textures as well as high resolution QuickBird and IKONOS satellite images validate the application of this approach for highly textured images. They also verify that the L*a*b* color space exhibits better performance.