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A Hybrid Transfer Learning Mechanism for Object Classification across View
Yi Mo; Zhaoxiang Zhang; Yunhong Wang
Conference Name11th International Conference on Machine Learning and Applications
Source PublicationICMLA 2012
Conference Date12-15 December 2012
Conference PlaceBoca Raton, Florida, USA
AbstractObject classification in traffic scene is of vital importance to intelligent traffic surveillance. In real applications, the shooting view changes frequently in different scenes, which leads to sharp accuracy decrease since source and target domain samples do not follow the same distribution anymore. On the other hand, manual labeling training samples is time and labor consuming. Transfer learning approaches are to utilize the knowledge learnt from source view for target object classification. In this paper, we propose a hybrid transfer learning mechanism combining two single transfer approaches to gap the divergence of different domain distributions. An instance-based transfer approach is implemented to label target samples that represent target domain distribution best. And a feature-based transfer framework is to learn a strong classifier for target domain with both labeled source and target domain samples. Experimental results indicate that our approach outperforms traditional machine learning and single transfer learning methods.
KeywordTransfer Learning Traffic Scene Surveillance Object Classification
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Yi Mo,Zhaoxiang Zhang,Yunhong Wang. A Hybrid Transfer Learning Mechanism for Object Classification across View[C],2012.
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