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6 results on '"Wu, Chengmao"'

5. A Novel Kernelized Total Bregman Divergence-Driven Possibilistic Fuzzy Clustering With Multiple Information Constraints for Image Segmentation.

Author

Wu, Chengmao and Zhang, Xue

Subjects
FUZZY algorithms, SIGNAL-to-noise ratio, MATHEMATICAL optimization, KERNEL functions, IMAGE segmentation, NOISE measurement, LINEAR programming
Abstract

Aiming at the problem that existing robust fuzzy clustering algorithms are still sensitive to high noise, a total Bregman divergence-driven possibilistic fuzzy clustering with multiple information constraints and kernel metric (TSKFLICM) is proposed in this article. First, we introduce total Bregman divergence (TBD) to overcome the shortcoming that Bregman divergence is variant with rotation. Second, polynomial kernel function is used to kernelize TBD, and kernelized TBD is embedded with neighborhood information of pixels to further enhance its ability to suppress noise. Finally, kernelized TBD with spatial information constraints is combined with possibilistic typicality to construct the novel objective function of possibilistic fuzzy clustering, and a TBD-driven kernel possibilistic fuzzy clustering with multiple information constraints is obtained through optimization theory. The effectiveness of the proposed algorithm for noisy image segmentation is explained by means of sample weighted clustering method. Experimental results show that compared with other algorithms, the Jaccard score, segmentation accuracy, and peak signal-to-noise ratio of the proposed algorithm are improved by 0.017–0.481, 1.327%–41.260%, and 2.416–11.765, respectively. Therefore, the TSKFLICM algorithm has better segmentation performance and stronger antinoise ability than the existing state-of-the-art fuzzy clustering algorithm. [ABSTRACT FROM AUTHOR]

Published
2022
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6. Generalized possibilistic c-means clustering with double weighting exponents.

Author

Wu, Chengmao and Yu, Dongxue

Subjects
*EXPONENTS, *DOCUMENT clustering, *MATHEMATICAL optimization, *PERFORMANCES
Abstract

• The generalized possibilistic clustering model with double exponents is established. • Two improved possibilistic clustering algorithms are proposed. • The convergence of the two proposed algorithms is strictly analyzed. • Experiment results indicate that the proposed algorithms have good performance. Considering that the improved possibilistic c-means (PCM) algorithms are sensitive to noise while addressing the issue of consistency clustering in PCM, this paper proposes the generalized possibilistic c-means clustering with double weighting exponents. Firstly, double weighting exponents are introduced into the PCM algorithm, and the generalized possibilistic c-means clustering model is established. Secondly, the difference between the double weighting exponents in the generalized possibilistic clustering model is set to 1 or −1; two improved single-exponent possibilistic clustering algorithms called IPCM1 and IPCM2 are proposed, and their local convergence is strictly proven by the Zangwill theorem. Finally, the reasonable range of the weighting exponent in IPCM1 and IPCM2 algorithms is determined by mathematical optimization. Experimental results indicate that IPCM1 and IPCM2 outperform existing PCM-related algorithms; they achieve excellent clustering performance, significantly improve the robustness to noise, and decrease the sensitivity to the weighting exponent. The work of this paper has far-reaching significance for promoting the development of the possibilistic c-means clustering theory. [ABSTRACT FROM AUTHOR]

Published
2023
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