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Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation

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Abstract

Multilevel thresholding is a very important image processing technique in the field of image segmentation. However, the computational complexity of determining the optimal threshold grows exponentially with increasing thresholds. To overcome this drawback, in this paper, we propose a multi-threshold image segmentation method based on the moth swarm algorithm. The meta-heuristic algorithm uses Kapur’s entropy method to optimize the thresholds for eight standard test images. When compared with other state-of-the-art evolutionary algorithms, the proposed method proved to be robust and effective according to numerical experimental results and image segmentation results. This indicates the high performance of the method for the segmentation of digital images.

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Acknowledgments

This work was supported by the National Science Foundation of China under Grant No. 61463007 and the Project of the Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380264. We thank Maxine Garcia, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac) for editing the English text of a draft of this manuscript.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Yongquan Zhou, Xiao Yang, Ying Ling & Jinzhong Zhang

  2. Key Laboratories of Guangxi High Schools Complex System and Computational Intelligence, Nanning, Guangxi, 530006, China

    Yongquan Zhou, Xiao Yang, Ying Ling & Jinzhong Zhang

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  1. Yongquan Zhou
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  2. Xiao Yang
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Correspondence to Yongquan Zhou.

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Zhou, Y., Yang, X., Ling, Y. et al. Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77, 23699–23727 (2018). https://doi.org/10.1007/s11042-018-5637-x

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  • DOI: https://doi.org/10.1007/s11042-018-5637-x

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