Markov random fields for texture classification
Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Markov random fields (MRFs) to homogeneous textures are evaluated and compared by visual examination and by standard pattern recognition methodology. The MRF model parameters capture the strong cues for human perception, such as directionality, coarseness, and/or contrast. This paper is a comparative study of MRF model-based features. A comparison of classifying natural textures and sandpaper textures using nearest neighbor (NN), quadratic, and Fisher classifiers, suggests that both texture feature extraction and classifier design should be simultaneously considered in designing an optimal texture classification system.