Skin color segmentation by texture feature extraction and K-mean clustering
Ng P.; Pun C.-M.
Source PublicationProceedings - 3rd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2011
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-meaning 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 93.8% and with false positives 28.4%. © 2011 IEEE.
KeywordK-mean clustering Skin segmentation Texture feature Wavelet transform
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Ng P.,Pun C.-M.. Skin color segmentation by texture feature extraction and K-mean clustering[C],2011:213-218.
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