UM
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
Fulltext Access
Document TypeJournal article
CollectionUniversity of Macau
AffiliationUniversidade de 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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