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Open AccessArticle

KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation

Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China
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Entropy 2018, 20(4), 273; https://doi.org/10.3390/e20040273
Received: 31 January 2018 / Revised: 13 March 2018 / Accepted: 28 March 2018 / Published: 12 April 2018
(This article belongs to the Special Issue Entropy-based Data Mining)
Ensemble clustering combines different basic partitions of a dataset into a more stable and robust one. Thus, cluster ensemble plays a significant role in applications like image segmentation. However, existing ensemble methods have a few demerits, including the lack of diversity of basic partitions and the low accuracy caused by data noise. In this paper, to get over these difficulties, we propose an efficient fuzzy cluster ensemble method based on Kullback–Leibler divergence or simply, the KL divergence. The data are first classified with distinct fuzzy clustering methods. Then, the soft clustering results are aggregated by a fuzzy KL divergence-based objective function. Moreover, for image segmentation problems, we utilize the local spatial information in the cluster ensemble algorithm to suppress the effect of noise. Experiment results reveal that the proposed methods outperform many other methods in synthetic and real image-segmentation problems. View Full-Text
Keywords: fuzzy clustering; ensemble learning; KL divergence; spatial information; image segmentation fuzzy clustering; ensemble learning; KL divergence; spatial information; image segmentation
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Wei, H.; Chen, L.; Guo, L. KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation. Entropy 2018, 20, 273.

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