A Hybrid Transfer Learning Mechanism for Object Classification across View

	A Hybrid Transfer Learning Mechanism for Object Classification across View
	Yi Mo; Zhaoxiang Zhang; Yunhong Wang
	2012-12-12
会议日期	12-15 December 2012
会议地点	Boca Raton, Florida, USA
关键词	Transfer Learning Traffic Scene Surveillance Object Classification
英文摘要	Object classification in traffic scene is of vital importance to intelligent traffic surveillance. In real applications, the shooting view changes frequently in different scenes, which leads to sharp accuracy decrease since source and target domain samples do not follow the same distribution anymore. On the other hand, manual labeling training samples is time and labor consuming. Transfer learning approaches are to utilize the knowledge learnt from source view for target object classification. In this paper, we propose a hybrid transfer learning mechanism combining two single transfer approaches to gap the divergence of different domain distributions. An instance-based transfer approach is implemented to label target samples that represent target domain distribution best. And a feature-based transfer framework is to learn a strong classifier for target domain with both labeled source and target domain samples. Experimental results indicate that our approach outperforms traditional machine learning and single transfer learning methods.
会议录	ICMLA 2012
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13254]
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. A Hybrid Transfer Learning Mechanism for Object Classification across View[C]. 见:. Boca Raton, Florida, USA. 12-15 December 2012.