Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning
文献类型：会议论文
| 作者 | Yi Mo; Zhaoxiang Zhang ; Yunhong Wang
|
| 出版日期 | 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
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13267]  |
| 专题 | 自动化研究所_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 | 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. |
入库方式： OAI收割
来源：自动化研究所
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