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A modified fuzzy clustering algorithm based on dynamic relatedness model for image segmentation

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Abstract

Accurate segmentation is the basis of object detection, computer vision and other fields. However, the complexity of images, together with the existence of noise and other image artifacts, makes image segmentation still a bottleneck. In this paper, a dynamic relatedness model is presented and an improved fuzzy clustering algorithm is proposed. Compared with traditional algorithms, the relatedness model is measured in the process of image segmentation, and can avoid the effect of inaccurate features in noisy images. With the help of the proposed relatedness, more accurate information can be adopted to enhance the results. Simulated experiments on various images demonstrate that the proposed algorithm can achieve satisfying results and is insensitive to noise of different types.

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Acknowledgements

This research was funded by NSF of China under Granted Number 61873117, 61903172, 62007017, 62106091, 62176140 and 62171209. The authors also gratefully acknowledge the reviewers’ helpful comments and suggestions, which will improve the presentation significantly.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, 264025, China

    Xin Gao, Yan Zhang, Hua Wang, Yujuan Sun & Xiaofeng Zhang

  2. Shandong Future Intelligent Financial Engineering Laboratory, Shandong Technology and Business University, Yantai, 264005, China

    Hua Wang, Feng Zhao & Xiaofeng Zhang

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  1. Xin Gao
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  2. Yan Zhang
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  3. Hua Wang
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  4. Yujuan Sun
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  5. Feng Zhao
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  6. Xiaofeng Zhang
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Correspondence to Xiaofeng Zhang.

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Gao, X., Zhang, Y., Wang, H. et al. A modified fuzzy clustering algorithm based on dynamic relatedness model for image segmentation. Vis Comput 39, 1583–1596 (2023). https://doi.org/10.1007/s00371-022-02430-4

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