Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models

A functional for unsupervised texture segmentation is investigated in this paper. An auto-normal model based on Markov Random Fields is employed here to represent textures. The functional investigated here is optimized with respect to the auto-normal model parameters and the evolving contour to simultaneously estimate auto-normal model parameters and find the evolving contour. Experimental results applied on the textures of the Brodatz album demonstrate the higher speed of convergence of this algorithm in comparison with a traditional stochastic algorithm in the literature.