Feature selection for texture recognition based on image synthesis

An efficient method for selection of features suitable for classification of textured images is presented. The spatial interaction of gray levels in a local neighbourhood N is modeled by stochastic random field models. The estimates of the model parameters are taken as textural features denoted by f_N. Selection of an N that would yield powerful features is done through visual examination of images synthesized using f_N. Experimental studies involving nine different types of natural textures yield 97% classification accuracy.