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A Markov Random Field Based Hybrid Algorithm with Simulated Annealing and Genetic Algorithm for Image Segmentation

  • Xinyu Du
  • Yongjie Li
  • Wufan Chen
  • Yi Zhang
  • Dezhong Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

In this paper, a simulated algorithm-genetic (SA-GA) hybrid algorithm based on a Markov Random Field (MRF) model (MRF-SA-GA) is introduced for image de-noising and segmentation. In this algorithm, a population of potential solutions is maintained at every generation, and for each solution a fitness value is calculated with a fitness function, which is constructed based on the MRF potential function according to Metropolis algorithm and Bayesian rule. Two experiments are selected to verify the performance of the hybrid algorithm, and the preliminary results show that MRF-SA-GA outperforms SA and GA alone.

Keywords

Image Segmentation Markov Random Field Crossover Rate Roulette Wheel Selection Markov Random 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xinyu Du
    • 1
  • Yongjie Li
    • 1
  • Wufan Chen
    • 1
  • Yi Zhang
    • 1
  • Dezhong Yao
    • 1
  1. 1.Center of NeuroInformatics, School of Life Science & TechnologyUniversity of Electronic Science and Technology of ChinaChengduPR China

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