Unsupervised segmentation of color textured images using a multilayer MRF model

Zoltan Kato, Ting-Chuen Pong, Song Guo Qiang

Abstract: Herein, we propose a novel multilayer Markov random field (MRF) image segmentation model which aims at combining color and texture features: each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn't restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.