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Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information

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Abstract

Robust image segmentation is a research hot point in recent years, and the segmentation of images corrupted by high noise is a challenging topic in this field. Picture fuzzy clustering is a novel potent computation intelligence method for pattern analysis and machine intelligence. Motivated by these, this paper aims to present a robust dynamic semi-supervised picture fuzzy clustering algorithm with spatial information constraints to meet the need for segmenting images with high noise. To explore the study, this paper firstly constructs a weighted square Euclidean distance by combining the current pixel with its neighborhood spatial information to measure the difference between the current pixel and the clustering center. Secondly, inspired by the idea of semi-supervised clustering, the weighted local membership information of the current pixel is embedded into the picture fuzzy clustering through KL divergence, and a novel dynamic semi-supervised fuzzy clustering is obtained. Thirdly, for further enhancement of the anti-noise ability of semi-supervised fuzzy clustering, a picture fuzzy local information factor is constructed by fusing picture fuzzy partition information with weighted square Euclidean distance and introduced into dynamic semi-supervised fuzzy clustering to form a robust picture fuzzy clustering-related algorithm for image segmentation in the presence of high noise. The experiments on a series of synthetic images, medical images, remote sensing images, and standard datasets illustrate how our proposed algorithm works. This proposed algorithm has excellent segmentation performance and anti-noise robustness and outperforms eight state-of-the-art fuzzy or picture fuzzy clustering-related algorithms on four standard datasets in the presence of high noise.

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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 (61671377,51709228) and the Shaanxi Natural Science Foundation of China (2016JM8034, 2017JM6107).

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Authors and Affiliations

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

    Chengmao Wu, Jiajia Zhang, Congcong Huang & Xiaokang Guo

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  1. Chengmao Wu
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Correspondence to Jiajia Zhang.

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Wu, C., Zhang, J., Huang, C. et al. Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information. Cogn Comput 14, 970–988 (2022). https://doi.org/10.1007/s12559-021-09988-6

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  • DOI: https://doi.org/10.1007/s12559-021-09988-6

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