FCM-based image segmentation with kernel functions

被引：1

作者：

Tang Xinting ^{[1
]}

Zhang Xiaofeng ^{[1
]}

Gao Hongjiang ^{[1
]}

Liu Kun ^{[2
]}

机构：

[1] Ludong Univ, Sch Informat & Elect Engn, Yantai 264025, Peoples R China

[2] Guangzhou Univ, Dept Comp Sci, Guangzhou 510006, Guangdong, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1 | 2017年

关键词：

Image segmentation; Fuzzy C-means; kernel function; C-MEANS ALGORITHM;

D O I：

10.1109/CSE-EUC.2017.186

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Image segmentation is an important problem in image processing and object recognition, and is one well-known bottleneck for further applications. Fuzzy C-means, as one typical clustering algorithm in pattern recognition, has been improved for image segmentation in many aspects. Aiming at the distance form in FCM, this paper proposes to incorporate FCM with kernel functions, which will make it insensitive to noise and other artifacts. Experiments show that the proposed algorithm can retrieve better results than other improved algorithms.

引用

页码：916 / 919

页数：4

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