A correlation structure based approach to neighborhood selection in random field models of texture images

Random field models have been successfully utilized in many applications requiring texture synthesis, classification, and segmentation. This class of models assumes each image pixel can be represented as a function of neighboring pixels and an additive noise sample. The effectiveness of these models is highly dependent on the choice of neighbor sets. Current approaches to selecting neighbor sets are based on ad-hoc methods. In the paper a systematic method which selects neighbor sets based on the correlation structure of texture images is presented and evaluated