Enhancing Cross-View Object Classification by Feature-Based Transfer Learning | |
Yi Mo; Zhaoxiang Zhang; Yunhong Wang | |
2012-11-11 | |
会议名称 | International Conference on Pattern Recognition |
会议录名称 | ICPR 2012 |
会议日期 | 11-15 November 2012 |
会议地点 | Tsukuba, Japan |
摘要 | 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. |
关键词 | Accuracy Vectors Training Surveillance Joints Manuals Pattern Recognition |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/13261 |
专题 | 模式识别实验室 |
通讯作者 | Zhaoxiang Zhang |
推荐引用方式 GB/T 7714 | Yi Mo,Zhaoxiang Zhang,Yunhong Wang. Enhancing Cross-View Object Classification by Feature-Based Transfer Learning[C],2012. |
条目包含的文件 | 条目无相关文件。 |
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