A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics.

Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented.