Texture discrimination using doubly stochastic Gaussian random fields

The authors propose a compound random field for texture discrimination called the doubly stochastic Gaussian (DSG) random field, to reduce isolated errors. Two major advantages of the DSG model are that it is easy to extract the features (the autoregressive parameters) and the a priori information can be incorporated into the model through the probability function of the lower level field. Experimental results on synthetic and natural images are presented. The results are quite good for the cases of both supervised and unsupervised models obtained from the simulated annealing algorithm and the HCF (highest confidence first) algorithm