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Multilevel Threshold Image Segmentation Based on Modified Moth-Flame Optimization Algorithm

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Advances in Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1422))

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Abstract

Among many image segmentation methods, multi-threshold segmentation is an effective method. However, the calculational complexity of multi-threshold segmentation is high, and the traditional calculation methods is difficult to obtain satisfactory results. In view of this situation, an Modified Moth-flame Optimization algorithm (MMFO) is proposed in this paper. Taking maximizing Kapur entropy as the objective function, MMFO algorithm is successfully applied to image multi-level threshold segmentation. The performance of the algorithm is evaluated by using several images which are widely used in the field of image segmentation. The experimental results show that, compared with other algorithms, MMFO algorithm can find a better solution more effectively.

This paper was supported in part by Project funded by China Postdoctoral Science Foundation under Grant 2020M671552, in part by Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 2019K223, in part by NUPTSF (NY220060), in part by the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software (No.2020DS301), in part by Natural Science Foundation of Jiangsu Province of China under Grant BK20191381, in part by the Natural Science Foundation of Anhui (1908085MF207), in part by the National Natural Science Foundation of China under Grant No. 61802207.

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Bin Xu, Yunkai Zhao, Chong Guo, Yuxin Yin & Jin Qi

  2. NanJing Pharmaceutical Co., Ltd., Nanjing, 210012, China

    Bin Xu

  3. Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China

    Bin Xu

Authors
  1. Bin Xu
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  2. Yunkai Zhao
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  3. Chong Guo
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  4. Yuxin Yin
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  5. Jin Qi
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Corresponding author

Correspondence to Bin Xu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Xu, B., Zhao, Y., Guo, C., Yin, Y., Qi, J. (2021). Multilevel Threshold Image Segmentation Based on Modified Moth-Flame Optimization Algorithm. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_22

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  • DOI: https://doi.org/10.1007/978-3-030-78615-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78614-4

  • Online ISBN: 978-3-030-78615-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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