MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.