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Skin color segmentation by Gaussian Mixture Models classifier and wavelet texture feature generation
Pun C.-M.; Ng P.
2014-10-01
Source PublicationInformation (Japan)
ISSN13448994 13434500
Volume17Issue:10APages:4937-4942
AbstractSkin Segmentation plays an important role in many computer vision applications. The aim of skin segmentation is to isolate skin regions in unconstrained input images. In this paper, a skin color segmentation approach by texture feature extraction and k-mean clustering is proposed. We improved the traditional skin classification by combining both color and texture features for skin segmentation. After the color segmentation using a 16 - Gaussian Mixture Models classifier, the texture features are extracted using effective wavelet transform with a 2-D Daubechies Wavelet and represented as a list of Shannon entropy. The non-skin regions can be eliminated by the Skin Texture-cluster Elimination using K-mean clustering. Experimental results based on common datasets show that our proposed can achieve better performance of the existing methods with true positive of 97.3% and with false positives 23.5% for the worst case, with true positive of 92.5% and with false positives 18.5% for the normal case.
KeywordK-mean clustering Skin segmentation Texture feature Wavelet transform
URLView the original
Language英語English
Fulltext Access
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Pun C.-M.,Ng P.. Skin color segmentation by Gaussian Mixture Models classifier and wavelet texture feature generation[J]. Information (Japan), 2014, 17(10A), 4937-4942.
APA Pun C.-M.., & Ng P. (2014). Skin color segmentation by Gaussian Mixture Models classifier and wavelet texture feature generation. Information (Japan), 17(10A), 4937-4942.
MLA Pun C.-M.,et al."Skin color segmentation by Gaussian Mixture Models classifier and wavelet texture feature generation".Information (Japan) 17.10A(2014):4937-4942.
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