A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics

Authors:
Kamal E. Melkemi;Mohamed Batouche;Sebti Foufou
Affiliations:
Computer Science Department, University of Biskra, 07000 Biskra, Algeria;LIRE Laboratory, University of Contantine, 25000 Constantine, Algeria;LE2I Laboratory, University of Burgundy, UFR sciences, BP 47870, 21078 Dijon, Cedex, France
Venue:
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Year:
2006

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Abstract

We propose a new distributed image segmentation algorithm structured as a multiagent system composed of a set of segmentation agents and a coordinator agent. Starting from its own initial image, each segmentation agent performs the iterated conditional modes method, known as ICM, in applications based on Markov random fields, to obtain a sub-optimal segmented image. The coordinator agent diversifies the initial images using the genetic crossover and mutation operators along with the extremal optimization local search. This combination increases the efficiency of our algorithm and ensures its convergence to an optimal segmentation as it is shown through some experimental results.