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MRF Parameter Estimation

  • S. Z. Li
Part of the Computer Science Workbench book series (WORKBENCH)

Abstract

A probabilistic distribution function has two essential elements: the form of the function and the involved parameters. For example, the joint distribution of an MRF is characterized by a Gibbs function with a set of clique potential parameters; and the noise by a zero-mean Gaussian distribution parameterized by a variance. A probability model is incomplete if the involved parameters are not all specified even if the functional form of the distribution is known. While formulating the forms of objective functions such as the posterior distribution has long been a subject of research for in vision, estimating the involved parameters has a much shorter history. Generally, it is performed by optimizing a statistical criterion, e.g. using existing techniques such as maximum likelihood, coding, pseudo-likelihood, expectation-maximization, Bayes.

Keywords

Partition Function Field Approximation Label Data Unlabeled Data Gibbs Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 1995

Authors and Affiliations

  • S. Z. Li
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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