Supervised Image Segmentation Based on Tree-Structured MRF Model in Wavelet Domain

Abstract

In the tree-structured Markov random field (TS-MRF) model, a sequence of MRFs was hierarchically defined on the single spatial resolution in the format of a tree structure which might suffer from the deficiency of modeling the nonstationary property of a given image. In order to overcome such a problem and motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we attempt to introduce the TS-MRF model into the wavelet domain and propose a new image modeling method-WTS-MRF, in which each MRF is defined over a multiresolution subset of the lattice sites corresponding to the wavelet decomposition. Based on WTS-MRF, a supervised image segmentation algorithm is carried out, and experiment on a remotely sensed image proves the better performance than the supervised segmentation algorithm based on the TS-MRF model.