Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 7
2200-2204,2218

Improved fuzzy C-means clustering algorithm based on Canopy clustering

Chen Kai
Chen Xiuhong
Sun Huiqiang
School of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2018.01.0116

Abstract

Aiming at the problem that the spatial influence factors are easily misidentified by noise in the fuzzy C-means algorithm for local spatial information(WFLICM), this paper proposed a fuzzy C-means clustering algorithm for image segmentation based on local and non-local spatial information(NLWFLICM). It introduced the non-local spatial information into the fuzzy influencing factor of WFLICM algorithm, and adaptively set the weight of local and non-local information according to the noise level, and re-marked the fuzzy influence factors of the central point. The experimental results show that the NLWFLICM algorithm is more robust and adaptive than the WFLICM algorithm, and improves the robustness of the WFLICM algorithm to a large extent, while preserving the image texture. In order to improve the clustering performance and convergence speed of the algorithm, combined with the advantages of Canopy algorithm for fast clustering of data, this paper proposed an improved algorithm for FCM image segmentation based on Canopy clustering and non-local spatial information(Canopy-NLWFLICM) before clustering algorithm. It can improve the convergence speed and image segmentation accuracy.

Foundation Support

国家自然科学基金资助项目(61373055)
2017年江苏省研究生科研创新计划项目(KYCX17_1500)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0116
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Technology of Graphic & Image
Pages: 2200-2204,2218
Serial Number: 1001-3695(2019)07-059-2200-05

Publish History

[2019-07-05] Printed Article

Cite This Article

陈凯, 陈秀宏, 孙慧强. 基于Canopy聚类的噪声自适应模糊C-均值算法 [J]. 计算机应用研究, 2019, 36 (7): 2200-2204,2218. (Chen Kai, Chen Xiuhong, Sun Huiqiang. Improved fuzzy C-means clustering algorithm based on Canopy clustering [J]. Application Research of Computers, 2019, 36 (7): 2200-2204,2218. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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