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Robust deep kernel-based fuzzy clustering with spatial information for image segmentation

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Abstract

Clustering algorithms with deep neural network has attracted wide attention to scholars. A deep fuzzy K-means clustering algorithm model on adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise. The research purpose of this paper is to focus on the anti-noise performance of image segmentation. Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61671377), the Shaanxi Natural Science Foundation of China (2021JM-459). The authors are thankful to the anonymous reviewers for their constructive suggestions to improve the overall quality of this paper.

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

    Lujia Lei, Chengmao Wu & Xiaoping Tian

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  1. Lujia Lei
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  2. Chengmao Wu
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  3. Xiaoping Tian
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Correspondence to Lujia Lei.

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Lei, L., Wu, C. & Tian, X. Robust deep kernel-based fuzzy clustering with spatial information for image segmentation. Appl Intell 53, 23–48 (2023). https://doi.org/10.1007/s10489-022-03255-3

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