Small-variance asymptotics of hidden Potts-MRFS: Application to fast Bayesian image segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a new approximate Bayesian estimator for hidden Potts-Markov random fields, with application to fast K-class image segmentation. The estimator is derived by conducting a small-variance-asymptotic analysis of an augmented Bayesian model in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. This leads to a new image segmentation methodology that can be efficiently implemented in large 2D and 3D scenarios by using modern convex optimisation techniques. Experimental results on synthetic and real images as well as comparisons with state-of-the-art algorithms confirm that the proposed methodology converges extremely fast and produces accurate segmentation results in only few iterations.

Original languageEnglish
Title of host publicationEuropean Signal Processing Conference
PublisherEURASIP
Pages1597-1601
Number of pages5
ISBN (Print)9780992862619
Publication statusPublished - 1 Jan 2014
Event22nd European Signal Processing Conference 2014 - Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014

Conference

Conference22nd European Signal Processing Conference 2014
Abbreviated titleEUSIPCO 2014
CountryPortugal
CityLisbon
Period1/09/145/09/14

Keywords

  • Bayesian methods
  • convex optimisation
  • Image segmentation
  • Potts Markov random field
  • spatial mixture models

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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