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2015 | OriginalPaper | Chapter

6. Markov Random Fields

Author : Luis Enrique Sucar

Published in: Probabilistic Graphical Models

Publisher: Springer London

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Abstract

This chapter presents an introduction to Markov random fields (MRFs), also known as Markov networks, which are undirected graphical models. We describe how a Markov random field is represented, including its structure and parameters, with emphasis on regular MRFs. Then, a general stochastic simulation algorithm to find the optimum configuration of an MRF is described, including some of its main variants. The problem of parameter estimation for an MRF is addressed, considering the maximum likelihood estimator. Conditional random fields are also introduced. The chapter concludes with two applications of MRFs for image analysis, one for image de-noising and the other for improving image annotation by including spatial relations.

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previous chapter Hidden Markov Models
next chapter Bayesian Networks: Representation and Inference
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Metadata
Title
Markov Random Fields
Author
Luis Enrique Sucar
Copyright Year
2015
Publisher
Springer London
Book
Probabilistic Graphical Models

Print ISBN: 978-1-4471-6698-6

Electronic ISBN: 978-1-4471-6699-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-1-4471-6699-3_6

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