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Hand posture recognition with co-training
Yikai Fang; Jian Cheng; Jinqiao Wang; Kongqiao Wang; Jing Liu; Hanqing Lu
Conference NameInternational Conference on Pattern Recognition
Source Publication
Conference DateDecember 8-11, 2008
Conference PlaceTampa, Florida, USA
AbstractAs an emerging human-computer interaction approachvision based hand interaction is more natural and efficient. Howeverin order to achieve high accuracy, most of the existing hand posture recognition methods need a large number of labeled samples which is expensive or unavailable in practice. In this paper, a co-training based method is proposed to recognize different hand postures with a small quantity of labeled data. Hand postures examples are represented with different features and disparate classifiers are trained simultaneously with labeled data. Then the semi-supervised learning treats each new posture as unlabeled data and updates the classifiers in a cotraining framework. Experiments show that the proposed method outperforms the traditional methods with much less labeled examples.
Document Type会议论文
Corresponding AuthorJian Cheng
Recommended Citation
GB/T 7714
Yikai Fang,Jian Cheng,Jinqiao Wang,et al. Hand posture recognition with co-training[C],2008.
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