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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15694

Title: A Multiagent System Approach for Image Segmentation Using Genetic Algorithms and Extremal Optimization Heuristics
Authors: Kamal E. Melkemi
Mohamed Batouche
Sebti Foufou
Keywords: Image segmentation; Markov random fields; Multiagent systems; Genetic algorithms; Extremal optimization
Issue Date: 2006
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
URI: http://hdl.handle.net/123456789/15694
Appears in Collections:College of Computer and Information Sciences

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