|Enhancing Cross-View Object Classification by Feature-Based Transfer Learning|
|Yi Mo; Zhaoxiang Zhang; Yunhong Wang
|Conference Name||International Conference on Pattern Recognition
|Source Publication||ICPR 2012
|Conference Date||11-15 November 2012
|Conference Place||Tsukuba, Japan
|Abstract||Object classification is of vital importance to intelligent traffic surveillance. A big challenge is that shooting view changes in different scenes, which leads to sharp accuracy decrease since training and test samples do not follow the same distribution anymore. On the other hand, manual labeling training samples is time and labor consuming. We propose a feature-based transfer learning framework to gap the divergence of different domain distributions with scarce target view samples. Source view samples, following a different but relevant distribution, could be utilized to learn what a good classifier is like by structure learning. At the same time, small amount of target view samples could make a great contribution to reflect the target distribution. Experimental results indicate that our method outperforms traditional approaches when target samples are too scarce to build a strong classifier.|
|Corresponding Author||Zhaoxiang Zhang|
Yi Mo,Zhaoxiang Zhang,Yunhong Wang. Enhancing Cross-View Object Classification by Feature-Based Transfer Learning[C],2012.
|Files in This Item:||
||There are no files associated with this item.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.