Classification of textures using Markov random field models

Abstract

Two feature extraction methods for classification of textures are presented. It is assumed that the given M × M texture is generated by a Gaussian Markov random field (GMRF) model, in the first method, the least square estimates of model parameters are used as features. In the second method, using the notion of sufficient statistics, it is shown that the sample correlations over a symmetric window including the origin are optimal features for classification. Simple minimum distance classifiers using these two feature sets yield classification accuracies of over 99% and 92% respectively for a seven class problem.