6533b7d6fe1ef96bd1266f15
RESEARCH PRODUCT
A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics
Mohamed BatoucheSebti FoufouKamal Eddine Melkemisubject
Extremal optimizationMathematical optimizationSegmentation-based object categorizationbusiness.industryMulti-agent systemCrossoverComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage segmentationComputingMethodologies_ARTIFICIALINTELLIGENCEComputer Science::Multiagent SystemsArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingSegmentationIterated conditional modesLocal search (optimization)Computer Vision and Pattern RecognitionbusinessAlgorithmSoftwareMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-08-01 | Pattern Recognition Letters |