UM
KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation
Wei, Huiqin; Chen, Long; Guo, Li
2018-04
Source PublicationENTROPY
ISSN1099-4300
Volume20Issue:4
AbstractEnsemble 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 duster 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.
Keywordfuzzy clustering ensemble learning KL divergence spatial information image segmentation
DOI10.3390/e20040273
URLView the original
Indexed BySCI
Language英语
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000435181600062
PublisherMDPI
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Wei, Huiqin,Chen, Long,Guo, Li. KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation[J]. ENTROPY,2018,20(4).
APA Wei, Huiqin,Chen, Long,&Guo, Li.(2018).KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation.ENTROPY,20(4).
MLA Wei, Huiqin,et al."KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation".ENTROPY 20.4(2018).
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