Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 814356, 18 pages
Review Article

Markov Models for Image Labeling

1College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China
2Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano (Sa), Italy

Received 2 May 2011; Accepted 18 May 2011

Academic Editor: Gani Aldashev

Copyright © 2012 S. Y. Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.