Twodimensional Cross Entropy Thresholding Based on Chaotic Resilient Particle Swarm Optimization or Decomposition

Abstract

Abstract: A twodimensional cross entropy image thresholding method based on chaotic resilient particle swarm optimization (CRPSO) or decomposition was proposed. Firstly, chaotic resilient particle swarm optimization was used to find the optimal threshold of twodimensional cross entropy method. The recursive algorithm was adopted to avoid the repetitive computation of fitness function in iterative procedure. As a result, the computing speed was improved greatly. Then, the computation of twodimensional cross entropy method was converted into two onedimensional spaces, which made the computation complexity further reduce from O(L2) to O(L). The experimental results show that, the two methods proposed in this paper can greatly reduce the running time while the segmented result is as good as or better than the existing twodimensional cross entropy thresholding method.

Key words: image segmentation, threshold selection, twodimensional cross entropy, chaotic resilient particle swarm optimization（CRPSO）, decomposition

CLC Number:

TN 911.73

WU Yi-Quan, ZHANG Xiao-Jie, WU Shi-Hua. Twodimensional Cross Entropy Thresholding Based on Chaotic Resilient Particle Swarm Optimization or Decomposition[J]. Journal of Shanghai Jiaotong University, 2011, 45(03): 301-0307.

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