%0 Journal Article
%A 曹卓
%A 吴成茂
%T 	Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation

%D 2021
%R 10.19682/j.cnki.1005-8885.2021.0013
%J 中国邮电高校学报（英文）
%P 24-40
%V 28
%N 1
%X <p style="text-align:justify;">
	To improve the anti-noise ability of fuzzy local information C-means &nbsp;clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means (FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.
</p>
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2021.0013