Texture segmentation based on a hierarchical Markov random field model

Abstract

A novel texture segmentation technique for both supervised and unsupervised segmentation is presented. The textured images under study are modeled by a proposed hierarchical Markov random field (MRF) model. This model is formed by combining the binomial model for textures and the multilevel logistic model for region distributions. The supervised segmentation is achieved by a novel algorithm which can reach the global maxima of the posteriori distribution even if the textures are modeled by an MRF model. For unsupervised segmentation, a novel parameter estimation scheme is proposed for estimating the model parameters directly from a given image. The proposed technique is verified by a variety of textured images, such as synthesized textures, natural textures, and aerial images, in both the supervised and unsupervised segmentation cases. >