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An adaptive gravitational search algorithm for multilevel image thresholding

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Abstract

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding.

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Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province of China, No. 2020A1515010784.

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Authors and Affiliations

  1. College of Software Engineering, Guangdong University of Science and Technology, Dongguan, 532000, Guangdong, China

    Yi Wang

  2. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China

    Zhiping Tan

  3. Department of Computer Science, University of California, Davis, CA, USA

    Yeh-Cheng Chen

Authors
  1. Yi Wang
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  2. Zhiping Tan
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  3. Yeh-Cheng Chen
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Correspondence to Zhiping Tan.

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Wang, Y., Tan, Z. & Chen, YC. An adaptive gravitational search algorithm for multilevel image thresholding. J Supercomput 77, 10590–10607 (2021). https://doi.org/10.1007/s11227-021-03706-7

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  • DOI: https://doi.org/10.1007/s11227-021-03706-7

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