Abstract:

A regional and statistical based algorithm for texture image segmentation was proposed. The Voronoi tessella-tion was used for partitioning the domain of an image into sub-regions corresponding to the components of homogenous regions, to which the texture image needs to be segmented. Bivariate Gaussian Markov random field (BGMRF) model, static random field, and potts model were employed to characterize the interactions between two neighbor pixel pairs in a sub-region, and among sub-regions, respectively. Following Bayesian paradigm, a posterior distribution, which models the texture segmentation for a given texture image, was obtained. A metropolis-hastings algorithm was designed for simulating the posterior distribution. Then, texture segmentation was obtained by maximum a posterior (MAP) scheme. The proposed algorithm was tested with both of synthesized and real texture images. The results are qualitatively and quantitatively evaluated and show that the proposed algorithm works well on both of texture images.

Key words: texture segmentation, Voronoi tessellation; bivariate Gaussian Markov random field (BGMRF), Bayesian inference; maximum a posterior (MAP)