The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (1): 24-40.doi: 10.19682/j.cnki.1005-8885.2021.0013

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Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation


  1. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
  • Received:2020-07-01 Revised:2020-11-23 Online:2021-02-28 Published:2021-03-28
  • Contact: Zhuo CAO
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61671377, 51709228), and the Natural
    Science Foundation of Shaanxi Province ( 2016JM8034, 2017JM6107).


Fuzzy clustering has been used widely in many fields, and its distance metric plays a key role in clustering performance. A new

To improve the anti-noise ability of fuzzy local information C-means  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.

Key words: fuzzy clustering, image segmentation, entropy-like divergence, robust clustering algorithm

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