CASIA OpenIR  > 类脑智能研究中心
Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning
Yi Mo; Zhaoxiang Zhang; Yunhong Wang
2012-09-30
Conference NameInternational Conference on Image Processing
Source PublicationICIP 2012
Conference DateSeptember 30 - October 3, 2012
Conference PlaceOrlando, FL, USA
AbstractObject 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.
KeywordTransductive Svm Traffic Scene Surveillance Object Classification Transfer Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13267
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Yi Mo,Zhaoxiang Zhang,Yunhong Wang. Cross-view Object Classification in Traffic Scene Surveillance Based on Transductive Transfer Learning[C],2012.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yi Mo]'s Articles
[Zhaoxiang Zhang]'s Articles
[Yunhong Wang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yi Mo]'s Articles
[Zhaoxiang Zhang]'s Articles
[Yunhong Wang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yi Mo]'s Articles
[Zhaoxiang Zhang]'s Articles
[Yunhong Wang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.