Unsupervised texture segmentation using selectionist relaxation

摘要

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 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.