Unsupervised Texture Segmentation

Abstract

We discussed three efficient and robust methods for unsupervised texture segmentation with unknown number of classes based on the underlying Markovian and GM texture models and their modifications for medical mammographics and remote sensing applications, respectively. Although these algorithm use the random field type models they are fast because they use efficient recursive or pseudo-likelihood parameter estimation of the underlying texture models and therefore they are much faster than the usual Markov