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Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

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Abstract

Aiming at the weak robustness of possibilistic fuzzy clustering against noise, a robust possibilistic fuzzy additive partition clustering with master–slave neighborhood information constraints is proposed for high noise image segmentation. This algorithm first constructs a master–slave neighborhood model, which consists of the master neighborhood window of the current pixel and the slave neighborhood window around the master neighborhood pixel. Then, the master–slave neighborhood information is integrated into the possibilistic fuzzy additive partition clustering model, and a novel robust possibilistic fuzzy clustering model incorporating deep local information is constructed. Next, this clustering model is further simplified by Cauchy inequality and a robust master–slave neighborhood information-driven possibilistic fuzzy clustering algorithm is derived by optimization theory. Extensive experimental results indicate that the proposed algorithm is very effective for noisy image segmentation, and its segmentation performance is significantly better than many existing state-of-the-art fuzzy clustering-related algorithms. In short, the work of this paper has profound significance for the development of robust fuzzy clustering theory.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, the authors would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China, for the financial support.

Funding

This work was supported by the National Natural Science Foundation of China (62071378) and the Shaanxi Natural Science Foundation of China (2022JM-370).

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

    Chengmao Wu & Wen Wu

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Chengmao Wu: Conceptualization, Methodology, Visualization, Investigation. Wen Wu: Data curation, Writing—original draft, Software, Validation, Writing—review & editing.

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Correspondence to Wen Wu.

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Wu, C., Wu, W. Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information. Circuits Syst Signal Process 43, 7662–7713 (2024). https://doi.org/10.1007/s00034-024-02758-3

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