Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey

2013

Article

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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 field 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 inference 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 significant 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.

Author(s): Chaohui Wang and Nikos Komodakis and Nikos Paragios
Journal: Computer Vision and Image Understanding (CVIU)
Volume: 117
Number (issue): 11
Pages: 1610-1627
Year: 2013
Month: November

Department(s): Perceiving Systems
Research Project(s): High-level Priors
Bibtex Type: Article (article)
Paper Type: Journal

Links: Publishers site
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BibTex

@article{MRFSurvey_CVIU2013,
  title = {Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey},
  author = {Wang, Chaohui and Komodakis, Nikos and Paragios, Nikos},
  journal = {Computer Vision and Image Understanding (CVIU)},
  volume = {117},
  number = {11},
  pages = {1610-1627},
  month = nov,
  year = {2013},
  month_numeric = {11}
}