| Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation |
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| 作者姓名： | Wu Chengmao Cao Zhuo |
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| 作者单位： | 西安邮电大学 |
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| 基金项目： | supported by the National Natural Science Foundation of China (61671377, 51709228);the Natural Science Foundation of Shaanxi Province (2016JM8034,2017JM6107)。 |
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| 摘 要： | 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.
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| 关 键 词： | fuzzy clustering image segmentation entropy-like divergence robust clustering algorithm |
| 收稿时间： | 2020-07-01 |
Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation |
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| Wu Chengmao,Cao Zhuo.Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation[J].The Journal of China Universities of Posts and Telecommunications,2021,28(1):24-40. |
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| Authors: | Wu Chengmao Cao Zhuo |
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| Affiliation: | School of Electronic Engineering, Xi'anUniversity of Posts and Telecommunications, Xi'an 710121, China |
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| Abstract: | 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. |
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