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Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning
Yi Mo; Zhaoxiang Zhang; Yunhong Wang
2012-09-30
会议名称International Conference on Image Processing
会议录名称ICIP 2012
会议日期September 30 - October 3, 2012
会议地点Orlando, FL, USA
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.
关键词Transductive Svm Traffic Scene Surveillance Object Classification Transfer Learning
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13267
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. Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning[C],2012.
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