Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

Marcelo Pereyra, Nick Whiteley, Christophe Andrieu, Jean Yves Tourneret

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

20 Citations (Scopus)

Abstract

This paper addresses the problem of estimating the Potts-Markov random field parameter β jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of β and maximum-a-posteriori unsupervised image segmentation. The method is based on a stochastic gradient adaptation technique whose computational complexity is significantly lower than that of the competing MCMC approaches. This adaptation technique can be easily integrated to existing MCMC methods where β was previously assumed to be known. Experimental results on synthetic data and on a real 3D real image show that the proposed method produces segmentation results that are as good as those obtained with state-of-the-art MCMC methods and at much lower computational cost.

Original languageEnglish
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
PublisherIEEE Computer Society
Pages121-124
Number of pages4
ISBN (Print)9781479949755
DOIs
Publication statusPublished - 2014
Event2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 - Gold Coast, QLD, Australia
Duration: 29 Jun 20142 Jul 2014

Conference

Conference2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
CountryAustralia
CityGold Coast, QLD
Period29/06/142/07/14

Keywords

  • Bayesian inference
  • Image segmentation
  • Intractable normalizing constants
  • Potts-Markov random field
  • Stochastic gradient Markov chain Monte Carlo

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    Pereyra, M., Whiteley, N., Andrieu, C., & Tourneret, J. Y. (2014). Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. In IEEE Workshop on Statistical Signal Processing Proceedings (pp. 121-124). [6884590] IEEE Computer Society. https://doi.org/10.1109/SSP.2014.6884590