Unsupervised Texture Segmentation Using Selectionist Relaxation

Abstract

We introduced an unsupervised texture segmentation method, the selectionist relaxation, relying on a Markov Random Field (MRF) texture description and a genetic algorithm based relaxation scheme. It has been shown elsewhere that this method is convenient for achieving a parallel and reliable estimation of MRF parameters and consequently a correct image segmentation. Nevertheless, these results have been obtained with an order 2 model on artificial textures. The purpose of the present work is to extend the use of this technique to higher orders and to show that it is suitable for the segmentation of natural textures, which require orders higher than 2 to be accurately described. The results reported here have been obtained using the generalized Ising model but the method can be easily transposed to other models.