2.4
Three important issues in MRF texture analysis are texture modeling, texture classification and texture segmentation. In MRF based texture modeling, a texture is assumed to be an MRF and to model a texture is to specify the corresponding conditional probabilities or Gibbs clique potential parameters. Texture classification is an application of pattern recognition techniques. It concerns the extraction of texture features and the design of a decision rule or classifier for classification. In MRF modeling, texture features correspond to the MRF texture parameters and feature extraction is equivalent to parameter estimation. In the supervised case, the estimation is performed using training data. This establishes reference parameters. Textures are classified by comparing texture feature vector extracted from the image and the reference feature vectors, which is basically a pattern recognition problem. Texture segmentation is to partition a textured image into regions such that each region contains a single texture and no two adjacent regions have the same texture. To do this, one has to deal with issues in texture modeling, parameter estimation and labeling. This chapter discusses problems in MRF texture modeling and segmentation.