One difficulty of textured image segmentation in the past was the lack of computationally efficient models which can capture statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on computational efficiency of multiscale representations have received attention. Most of previous researches have been based on multiscale stochastic models which use the Gaussian pyramid decomposition as image decomposition scheme. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wave let representation, we present an unsupervised textured image segmentation algorithm which is based on a multiscale stochastic modeling over the wavelet decomposition of image. For the sake of computational efficiency, versions of the EM algorithm and MAP estimate, which are based on the mean-field decomposition of a posteriori probability, are used for estimating model parameters and the segmented image, respectively.
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