Bayesian Image Classification with Baddeley’s Delta Loss

Arnoldo Frigessi, Håvard Rue*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this article we adopt Baddeley's delta metric as a loss function in Bayesian image restoration and classification. We develop a new algorithm that allows us to approximate the corresponding optimal Bayesian estimator. With this algorithm good practical estimates can be obtained at approximately the same computational cost as traditional estimators like the marginal posterior mode (MPM). A comparison of our proposed classification with MPM shows significant advantages, especially with respect to fine structures.

Original languageEnglish (US)
Pages (from-to)55-73
Number of pages19
JournalJournal of Computational and Graphical Statistics
Volume6
Issue number1
DOIs
StatePublished - 1997
Externally publishedYes

Keywords

  • Asymmetric loss functions
  • Bayesian inference
  • Distance between binary images
  • Image restoration
  • Markov chain Monte Carlo methods
  • Metropolis algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics

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