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Modified salp swarm algorithm based multilevel thresholding for color image segmentation

  • Received: 18 April 2019 Accepted: 02 August 2019 Published: 24 October 2019
  • This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.

    Citation: Shikai Wang, Heming Jia, Xiaoxu Peng. Modified salp swarm algorithm based multilevel thresholding for color image segmentation[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 700-724. doi: 10.3934/mbe.2020036

    Related Papers:

  • This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.
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    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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