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Suppressed robust picture fuzzy clustering for image segmentation

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Abstract

With the increase in sample number or image size, it is very important to improve the efficiency of picture fuzzy clustering algorithm in data classification or image segmentation. This paper presents an algorithm to improve the convergence speed of picture fuzzy clustering for large-scale data. According to the existing suppression fuzzy clustering idea, the maximum value of picture fuzzy partition information of picture fuzzy clustering is further increased and other values are decreased. The modified picture fuzzy partition information is used to update the cluster center of the sample, which speeds up the convergence speed of the algorithm. The adaptive selection of suppression factors makes the picture fuzzy clustering obtain faster operation efficiency and better segmentation performance. In order to further improve the performance of picture fuzzy clustering algorithm for noise image segmentation, an adaptive robust suppression picture fuzzy clustering algorithm with neighborhood spatial information constraints is proposed. Experimental results show that the proposed algorithm not only shortens the operation speed of existing picture fuzzy clustering, but also improves the robustness to noise.

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Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (61671377, 51709228) and the Natural Science Foundation of Shaanxi Province (2016JM8034, 2017JM6107).

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Authors and Affiliations

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Chengmao Wu & Na Liu

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  1. Chengmao Wu
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  2. Na Liu
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Correspondence to Na Liu.

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Wu, C., Liu, N. Suppressed robust picture fuzzy clustering for image segmentation. Soft Comput 25, 3751–3774 (2021). https://doi.org/10.1007/s00500-020-05403-8

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