标题:Patch-based fuzzy clustering for image segmentation
作者:Zhang, Xiaofeng; Guo, Qiang; Sun, Yujuan; Liu, Hui; Wang, Gang; Su, Qingtang; Zhang, Caiming
作者机构:[Zhang, Xiaofeng; Sun, Yujuan; Wang, Gang; Su, Qingtang; Zhang, Caiming] Ludong Univ, Sch Informat & Elect Engn, Yantai 254025, Peoples R China.; [Z 更多
通讯作者:Zhang, XF;Zhang, XF;Zhang, Xiaofeng
通讯作者地址:[Zhang, XF]Ludong Univ, Sch Informat & Elect Engn, Yantai 254025, Peoples R China;[Zhang, XF]Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jin 更多
来源:SOFT COMPUTING
出版年:2019
卷:23
期:9
页码:3081-3093
DOI:10.1007/s00500-017-2955-2
关键词:Image segmentation; Fuzzy clustering; FLICM; Pixel relevance; Patch; similarity
摘要:Fuzzy C-means has been adopted for image segmentation, but it is sensitive to noise and other image artifacts due to not considering neighbor information. In order to solve this problem, many improved algorithms have been proposed, such as fuzzy local information C-means clustering algorithm (FLICM). However, the segmentation results of FLICM are unsatisfactory when performed on complex images. To overcome this, a novel fuzzy clustering algorithm is proposed in this paper, and more information is utilized to guide the procedure of image segmentation. In the proposed algorithm, pixel relevance based on patch similarity will be investigated firstly, by which all information over the whole image can be considered, not limited to local context. Compared with Zhang et al. (Multimed Tools Appl 76(6):7869-7895, 2017a. 10.1007/s11042-016-3399-x) pixel relevance is unnecessary to be normalized, and much more information can play positive role in image segmentation. Experimental results show that the proposed algorithm outperforms current fuzzy algorithms, especially in enhancing the robustness of corresponding fuzzy clustering algorithms.
收录类别:EI;SCIE
资源类型:期刊论文
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