Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure o...

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Published in:	Symmetry (Basel) Vol. 11; no. 6; p. 753
Main Authors:	Zhang, Wenyuan, Guo, Xijuan, Huang, Tianyu, Liu, Jiale, Chen, Jun
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2019
Subjects:	Algorithms Artificial intelligence Background noise Bias Clustering Computation Distance measurement Euclidean geometry Fuzzy sets Image segmentation Kernels Neighborhoods Noise Outliers (statistics) Robustness (mathematics)
ISSN:	2073-8994, 2073-8994
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Abstract	The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.
AbstractList	The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.
Author	Chen, Jun Zhang, Wenyuan Liu, Jiale Huang, Tianyu Guo, Xijuan
Author_xml	– sequence: 1 givenname: Wenyuan surname: Zhang fullname: Zhang, Wenyuan – sequence: 2 givenname: Xijuan surname: Guo fullname: Guo, Xijuan – sequence: 3 givenname: Tianyu orcidid: 0000-0001-7495-4489 surname: Huang fullname: Huang, Tianyu – sequence: 4 givenname: Jiale surname: Liu fullname: Liu, Jiale – sequence: 5 givenname: Jun surname: Chen fullname: Chen, Jun
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Snippet	The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity...
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SubjectTerms	Algorithms Artificial intelligence Background noise Bias Clustering Computation Distance measurement Euclidean geometry Fuzzy sets Image segmentation Kernels Neighborhoods Noise Outliers (statistics) Robustness (mathematics)
Title	Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm
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