Texture classification using features derived from random field models

This paper presents a new feature extraction method for classifying a texture image into one of the l possible classes C-i, i=1, ..., l. It is assumed that the given M x M image characterized by a set of intensity levels, {y(s(1), s(2)), 0 <= s(1), s(2) <= M - 1}, is a realization of an underlying random field model, known as the Simultaneous Autoregressive Model (SAR). This model is characterized by a set of parameters phi whose probability density function p(i)(phi), depends on the class to which the image belongs. First it is shown that the maximum likelihood estimate (M.L.E.) phi*, of phi is an appropriate feature vector for classification purposes. The optimum Bayes classifier which minimizes the average probability of classification error, is then designed using phi*. Finally the efficiency of the feature vector is demonstrated through experimental results obtained with some natural texture data and a simpler quadratic mean classifier.