Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning

	Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning
	Yi Mo; Zhaoxiang Zhang; Yunhong Wang
	2012-09-30
会议日期	September 30 - October 3, 2012
会议地点	Orlando, FL, USA
关键词	Transductive Svm Traffic Scene Surveillance Object Classification Transfer Learning
英文摘要	Object classification in traffic scene surveillance has been a hot topic in image processing field. A big challenge is that shooting view changes in different scenes, which leads to sharp accuracy decrease since training and test samples do not share the same distribution. Inductive transfer learning methods try to bridge this gap by making use of manually labeled target samples. However, it is in line with reality to conduct unsupervised transfer without manually labeling. In this paper, we propose an intuitive transductive transfer method by transferring instances across view. Experimental results indicate that our method outperforms traditional approaches such as inductive SVM and cluster method, and could even achieve a comparable performance compared with manually labeling approach.
会议录	ICIP 2012
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13267]
专题	自动化研究所_类脑智能研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Yi Mo,Zhaoxiang Zhang,Yunhong Wang. Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning[C]. 见:. Orlando, FL, USA. September 30 - October 3, 2012.