Affiliations: University of Biskra, Computer Science Department, 07000 Biskra, Algeria and University of Constantine, Vision Group, LIRE laboratory, 25000 Constantine, Algeria, e-mail: [email protected] | University of Constantine, Vision Group, LIRE laboratory, 25000 Constantine, Algeria, e-mail: [email protected] | University of Burgundy, LE2I laboratory, UFR sciences, BP 47870, 21078 Dijon Cedex, France, e-mail: [email protected]
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.
Keywords: image segmentation, Markov random fields, multiresolution, Bak–Sneppen, self-organized criticality
Journal: Informatica, vol. 17, no. 2, pp. 225-236, 2006
