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Semi-supervised Learning in Traffic Scene Surveillance Based on Label-propagation
Meng Liang; Zhaoxiang Zhang; Yunhong Wang
2013-09-15
Conference NameInternational Conference on Image Processing
Source PublicationICIP 2013
Conference Date15-18 September 2013
Conference PlaceMelbourne, Australia
AbstractObject classification in traffic scene surveillance has attracted much attention recent years. Traditional classification methods need lots of labeled samples to build a satisfying classifier. However, the acquisition of the labeled samples may cost lots of time and human labor. In this paper, we propose an label-propagation based semi-supervised learning method which uses the information of both labeled and un-labeled samples. Experiment results show that our method outperforms the traditional methods both in accuracy and robustness.
KeywordSemi-supervised Learning Traffic Scene Surveillance Object Classification Label Propagation
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13287
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Meng Liang,Zhaoxiang Zhang,Yunhong Wang. Semi-supervised Learning in Traffic Scene Surveillance Based on Label-propagation[C],2013.
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