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

Kamal E. Melkemi, Mohamed Batouche, Sebti Foufou

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    Pages (from-to)1230-1238
    Number of pages9
    JournalPattern Recognition Letters
    Volume27
    Issue number11
    DOIs
    StatePublished - Aug 1 2006

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    Multi agent systems
    Image segmentation
    Genetic algorithms
    Mathematical operators

    Keywords

    • Extremal optimization
    • Genetic algorithms
    • Image segmentation
    • Markov random fields
    • Multiagent systems

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

    A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics. / Melkemi, Kamal E.; Batouche, Mohamed; Foufou, Sebti.

    In: Pattern Recognition Letters, Vol. 27, No. 11, 01.08.2006, p. 1230-1238.

    Research output: Contribution to journalArticle

    Melkemi, Kamal E. ; Batouche, Mohamed ; Foufou, Sebti. / A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics. In: Pattern Recognition Letters. 2006 ; Vol. 27, No. 11. pp. 1230-1238.
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