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Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation
Joint Authors
Yang, Yao
Wu, Chengmao
Li, Yawen
Zhang, Shaoyu
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-22, 22 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-23
Country of Publication
Egypt
No. of Pages
22
Main Subjects
Abstract EN
To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper.
The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem.
Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced.
Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.
American Psychological Association (APA)
Yang, Yao& Wu, Chengmao& Li, Yawen& Zhang, Shaoyu. 2020. Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-22.
https://search.emarefa.net/detail/BIM-1196096
Modern Language Association (MLA)
Yang, Yao…[et al.]. Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation. Mathematical Problems in Engineering No. 2020 (2020), pp.1-22.
https://search.emarefa.net/detail/BIM-1196096
American Medical Association (AMA)
Yang, Yao& Wu, Chengmao& Li, Yawen& Zhang, Shaoyu. Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-22.
https://search.emarefa.net/detail/BIM-1196096
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196096