MRF Energy Minimization for Unsupervised Image Segmentation
Qiuxu Li, Jieyu Zhao · 2009
A Markov random field (MRF) model is proposed for unsupervised image segmentation in this paper. The theoretical framework is based on Bayesian estimation via the graph-cut energy optimization method. A Gaussian is used to model the density associated with each image segment (or class), and parameters are estimated with an expectation maximization (EM) algorithm. Here we use the perceptually uniform CIELAB color values instead of the RGB color. Graph cuts have emerged as a powerful optimization technique for minimizing MRF energy functions that arise in low-level vision problems. We adopt a new min-cut/max-flow algorithm which works several times faster than any of the other max-flow methods, which makes near real-time performance possible. Experimental results have been provided to illustrate the performance of our method.