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A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation

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Abstract

Two-dimensional (2D) multilevel thresholding is an important technique for noisy image segmentation which has drawn much attention during the past few years. The conventional image segmentation methods are efficient for 2D bi-level thresholding. However, the computational complexity grows exponentially when extended to 2D multilevel thresholding since they search the optimal thresholds by exhaustive strategy. To tackle this problem, a fuzzy adaptive gravitational search algorithm (FAGSA) using Tsallis entropy as its objective function has been presented to find the optimal 2D multilevel thresholds in this paper. In the FAGSA, fuzzy logic controllers are designed to tune the control parameters. The state-of-the-art heuristic algorithms are compared with this proposed algorithm. Both test images and noisy images are utilized in the experiments to evaluate the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our algorithm in terms of the objective function value, image quality measures and time consumption.

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Acknowledgements

This work is supported by GDAS' Project of Science and Technology Development (2019GDASYL- 0103077, 2018GDASCX-0115, 2017GDASCX-0115).

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Authors and Affiliations

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

    Zhiping Tan

  2. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, 510070, China

    Dongbo Zhang

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  1. Zhiping Tan
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  2. Dongbo Zhang
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Correspondence to Dongbo Zhang.

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Tan, Z., Zhang, D. A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation. J Ambient Intell Human Comput 11, 4983–4994 (2020). https://doi.org/10.1007/s12652-020-01777-7

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  • DOI: https://doi.org/10.1007/s12652-020-01777-7

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