Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44364
標題: Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation
作者: Juang, C.F.
莊家峰
Chiu, S.H.
Shiu, S.J.
關鍵字: color segmentation;fuzzy clustering;fuzzy neural network (FNN);mixture of Gaussian classifier (MGC);structure learning;image segmentation;networks
Project: Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans
期刊/報告no:: Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans, Volume 37, Issue 6, Page(s) 1077-1087.
摘要: 
This paper proposes a Fuzzy System learned through Fuzzy Clustering and Support Vector Machine (FS-FCSVM). The FS-FCSVM is a fuzzy system constructed by fuzzy if-then rules with fuzzy singletons in the consequence. The structure of FS-FCSVM is constructed by fuzzy clustering on the input data, which helps to reduce the number of rules. Parameters in FS-FCSVM are learned through a support vector machine (SVM) for the purpose of achieving higher generalization ability. In contrast to nonlinear kernel-based SVM or some other fuzzy systems with a support vector learning mechanism, both the number of parameters/rules in FS-FCSVM and the computation time are much smaller. FS-FCSVM is applied to skin color segmentation. For color information representation, different types of features based on scaled hue and saturation color space are used. Comparisons with a fuzzy neural network, the Gaussian kernel SVM, and mixture of Gaussian classifiers are performed to show the advantage of FS-FCSVM.
URI: http://hdl.handle.net/11455/44364
ISSN: 1083-4427
DOI: 10.1109/tsmca.2007.904579
Appears in Collections:電機工程學系所

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