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A new image segmentation method based on the ICSO-ISPCNN model

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Abstract

To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm.

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Acknowledgments

This work is supported by Hainan Provincial Natural Science Foundation of China (618QN220), the Agricultural Science and Technology Innovation and Public Relations project of Shaanxi Province of China (2016NY-176), and the National Natural Science Foundation of China (61877038, 61373120).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Jianhui Liang & Lifang Wang

  2. College of Applied Science and Technology, Hainan University, Danzhou, 571737, China

    Jianhui Liang

  3. School of Computer Science, Shaanxi Normal University, Xi’an, 710062, China

    Miao Ma

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  1. Jianhui Liang
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  2. Lifang Wang
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Correspondence to Lifang Wang or Miao Ma.

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Liang, J., Wang, L. & Ma, M. A new image segmentation method based on the ICSO-ISPCNN model. Multimed Tools Appl 79, 28131–28154 (2020). https://doi.org/10.1007/s11042-019-08596-9

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