A multiresolution approach based on MRF and Bak-Sneppen models for image segmentation

Kamal E. Melkemi, Mohamed Batouche, Sebti Foufou

Research output: Contribution to journalArticle

Abstract

The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase. In this paper, we combine Bak-Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak-Sneppen model. The a-posteriori probability corresponds to a local fitness. At each cycle, some objectionable species are chosen for a random change in their fitness values. Furthermore, the change in the fitness of each species engenders fitness changes for its neighboring species. After a certain number of iteration, the system converges to a Maximum A Posteriori estimate. In this multireolution approach, we use a wavelet transform to reduce the size of the system.

Original languageEnglish (US)
Pages (from-to)225-236
Number of pages12
JournalInformatica
Volume17
Issue number2
StatePublished - Jan 1 2006

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Keywords

  • Bak-Sneppen
  • Image segmentation
  • Markov random fields
  • Multiresolution
  • Self-organized criticality

ASJC Scopus subject areas

  • Information Systems
  • Applied Mathematics

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