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http://dx.doi.org/10.5391/JKIIS.2007.17.2.196

A Density Estimation based Fuzzy C-means Algorithm for Image Segmentation  

Ko, Jeong-Won (한양대학교 전자 컴퓨터 제어 계측 공학과)
Choi, Byung-In (한양대학교 전자 컴퓨터 제어 계측 공학과)
Rhee, Frank Chung-Hoon (한양대학교 전자 컴퓨터 제어 계측 공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.2, 2007 , pp. 196-201 More about this Journal
Abstract
The Fuzzy E-means (FCM) algorithm is a widely used clustering method that incorporates probabilitic memberships. Due to these memberships, it can be sensitive to noise data. In this paper, we propose a new fuzzy C-means clustering algorithm by incorporating the Parzen Window method to include density information of the data. Several experimental results show that our proposed density-based FCM algorithm outperforms conventional FCM especially for data with noise and it is not sensitive to initial cluster centers.
Keywords
Fuzzy c-means; Noise; Density Estimation; Parzen-window;
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