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A Hybrid Transfer Learning Mechanism for Object Classification across View
Yi Mo; Zhaoxiang Zhang; Yunhong Wang
2012-12-12
会议名称11th International Conference on Machine Learning and Applications
会议录名称ICMLA 2012
会议日期12-15 December 2012
会议地点Boca Raton, Florida, USA
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.
关键词Transfer Learning Traffic Scene Surveillance Object Classification
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13254
专题类脑智能研究中心
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],2012.
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