Unsupervised Image Segmentation Based on MRFs and Graph Cuts
Qiuxu Li, Jieyu Zhao · Journal of Communication and Computer · 2009
Markov random fields (MRFs) can be used for a wide variety of vision problems. In this paper we will propose a Markov random field (MRF) image segmentation model. The theoretical framework is based on Bayesian estimation via the energy optimization. Graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in low-level vision problem. The theorem of Ford and Fulkerson states that mill-cut and max-flow problems are equivalent. So, the minimum s/t cut problem can be solved by finding a maximum flow from the source s to the sink t. we adopt a new mm-cut/max-flow algorithm which belongs to the group of algorithms based on augmenting paths. We propose a parameter estimation method using expectation maximization (EM) algorithm. We also choose Gaussian mixture model as our image model and model the density associated with one of image segments (or classes) as a multivariate Gaussian distribution. Characteristic features related to the information in color, texture and position are extracted for each pixel. Experimental results will be provided to illustrate the performance of our method.