Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

Chaohui Wang, Nikos Komodakis, Nikos Paragios
2013 Computer Vision and Image Understanding  
In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision eld about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as
more » ... nce and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed signi cant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.
doi:10.1016/j.cviu.2013.07.004 fatcat:d4ruu3u4gvg3dmud7xhni5gpbq