Semi-supervised Learning in Traffic Scene Surveillance Based on Label-propagation

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	Semi-supervised Learning in Traffic Scene Surveillance Based on Label-propagation
	Meng Liang; Zhaoxiang Zhang; Yunhong Wang
	2013-09-15
会议名称	International Conference on Image Processing
会议录名称	ICIP 2013
会议日期	15-18 September 2013
会议地点	Melbourne, Australia
摘要	Object 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.
关键词	Semi-supervised Learning Traffic Scene Surveillance Object Classification Label Propagation
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13287
专题	智能感知与计算研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Meng Liang,Zhaoxiang Zhang,Yunhong Wang. Semi-supervised Learning in Traffic Scene Surveillance Based on Label-propagation[C],2013.