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Robust fuzzy clustering algorithms integrating membership guided image filtering

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Abstract

Aiming at the shortcoming of existing fuzzy clustering in noise suppression, the combination of fuzzy clustering and guided filtering can improve the ability of noise suppression to some extent. Although image guided membership filter achieves better anti-noise performance, it still does not meet the needs of images with high noise. In this paper, membership guided image filter is firstly introduced into kernel-based fuzzy local information clustering (KWFLICM), and a novel multi-objective optimization model for robust fuzzy clustering is constructed. Then least square method is used to solve the optimization model, and the iterative algorithm for noisy image segmentation is obtained. Experimental results show that the proposed algorithm has better segmentation performance and robustness compared with existing many robust fuzzy clustering algorithms and fuzzy clustering algorithms which use image to guide membership filtering.

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Funding

The funding was provided by National Natural Science Foundation of China (Grand Nos. 61671377, 51709228).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710100, China

    Caicai Qiao & Jiaye Wang

  2. School of Electronics and Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710100, China

    Chengmao Wu

  3. School of Science, Xi’an University of Posts and Telecommunications, Xi’an, 710100, China

    ·Changxing Li

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  1. Caicai Qiao
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  2. Chengmao Wu
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  3. ·Changxing Li
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  4. Jiaye Wang
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Correspondence to Caicai Qiao.

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Qiao, C., Wu, C., Li, ·. et al. Robust fuzzy clustering algorithms integrating membership guided image filtering. SIViP 16, 1851–1859 (2022). https://doi.org/10.1007/s11760-022-02144-z

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