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An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

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  • Jian Zhang
  • Huanzhou Li
  • Zhangguo Tang
  • Qiuping Lu
  • Xiuqing Zheng
  • Jiliu Zhou

Abstract

A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

Suggested Citation

  • Jian Zhang & Huanzhou Li & Zhangguo Tang & Qiuping Lu & Xiuqing Zheng & Jiliu Zhou, 2014. "An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-12, April.
    • Handle: RePEc:hin:jnlmpe:295402
    DOI: 10.1155/2014/295402
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