A segmentation method for textured images based on the maximum posterior mode criterion

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Abstract

We consider the problem of semi-supervised segmentation of textured images. Recently, reweighted belief propagation has been introduced as a solution for Bayesian inference with respect to the maximum posterior mode criterion. In this paper, we show how to adapt reweighted belief propagation to the problem of segmentation of textured images. An adaptive parameter estimation technique is also provided. Then, we compare classical simulated annealing with the recently introduced reweighted belief propagation algorithm, in terms of segmentation results.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2088-2091
Number of pages4
DOIs
Publication statusPublished - 18 Aug 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Gauss-Markov random field
  • Markov random field
  • Texture segmentation
  • graphical models
  • reweighted belief-propagation
  • simulated annealing

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