Modified salp swarm algorithm based multilevel thresholding for color image segmentation

Shikai Wang; Heming Jia; Xiaoxu Peng; Shikai Wang; Heming Jia; Xiaoxu Peng

doi:10.3934/mbe.2020036

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 700-724. doi: 10.3934/mbe.2020036

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Modified salp swarm algorithm based multilevel thresholding for color image segmentation

1.
School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China
2.
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

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.
- multi-threshold color image segmentation,
- Kapur's entropy method,
- slap swarm optimization,
- levy flight
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

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Abstract

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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