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Contextual estimators of mixing probabilities for Markov chain random fields

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
26
Issue
5
Identifiers
DOI: 10.1016/0031-3203(93)90129-k
Keywords
  • Bias
  • Estimation
  • Image Analysis
  • Markov Chain
  • Prior Probabilities
  • Random Field
  • Training Set
Disciplines
  • Mathematics

Abstract

Abstract This paper discusses the estimation of proportions of classes from an image. Classification methods are compared with likelihood methods and the importance of contextual information is discussed. The joint distribution of classes at neighbouring sites is modelled by a Markov chain random field. The class attributes are estimated from training sets and unclassified observations. The effect of biased class means is reduced with a stochastic model of the bias. Contextual likelihood methods yield better results than non-contextual methods.

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