Abstract
Markov Random Fields (MRFs) can be used for a wide variety of vision problems. In this paper we focus on MRFs with two-valued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut problem on a graph. We develop efficient algorithms for computing good approximations to the minimum multiway, cut. The visual correspondence problem can be formulated as an MRF in our framework; this yields quite promising results on real data with ground truth. We also apply our techniques to MRFs with linear clique potentials.
Keywords
Markov random fieldMaximum a posteriori estimationMarkov chainFocus (optics)CliqueA priori and a posterioriRandom fieldComputer scienceRandom graphGraphClique problemMathematical optimizationMarkov processAlgorithmMathematicsArtificial intelligenceTheoretical computer scienceMachine learningImage (mathematics)CombinatoricsMaximum likelihoodImage segmentation
Affiliated Institutions
Related Publications
Fast Approximate Maximum <i>A Posteriori</i> Restoration of Multicolour Images
SUMMARY We propose a new algorithm for the approximation of the maximum a posteriori (MAP) restoration of noisy images. The image restoration problem is considered in a Bayesian...
The mean field theory in EM procedures for blind Markov random field image restoration
A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF...
Unsupervised image segmentation
We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a hierarchical Markov random field (MRF...
Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields
In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have ab...
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The app...
Publication Info
- Year
- 2002
- Type
- article
- Pages
- 648-655
- Citations
- 411
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
411
OpenAlex
Cite This
Yuri Boykov,
Olga Veksler,
Ramin Zabih
(2002).
Markov random fields with efficient approximations.
, 648-655.
https://doi.org/10.1109/cvpr.1998.698673
Identifiers
- DOI
- 10.1109/cvpr.1998.698673