Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
View independent vehicle/person classification
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Cast Shadow Removal with GMM for Surface Reflectance Component
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We discuss the problem of object classification in cross-view traffic scene surveillance videos in this paper. To classify moving objects in traffic scene videos into pedestrian, bicycle and variety of vehicles, an effective intelligent classification framework has been proposed which takes advantage of a transfer machine learning method to bridge the gap between source scene data and target scene data. The transfer learning algorithm makes one classifier adaptive to perspective changes instead of training two different classifiers for corresponding perspectives. The samples transferred from source scene database have saved much manual labeling work on target scene database. In this paper, we propose an active transfer learning method to decrease manual labeling work further for target scene traffic object classification. Redundant experiments are conducted and experimental results demonstrate the effectiveness and convenience of our approach.