Learning instance-to-class distance for human action recognition

Authors:
Zhengxiang Wang;Yiqun Hu;Liang-Tien Chia
Affiliations:
Center for Multimedia and Network Technology, School of Computer Engineering, Nanyang Technological University, Singapore;Center for Multimedia and Network Technology, School of Computer Engineering, Nanyang Technological University, Singapore;Center for Multimedia and Network Technology, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

In this paper, we propose a large margin framework to learn the local instance-to-class distance function using local patch-based feature vectors, which satisfies the property that distance from instance to its own class should be less than the distance to other class. This instance-to-class distance is modeled as the weighted combination of the distance from every patch in test image to its nearest patch in training class, where the weight is learned through the above learning phase. We evaluate the proposed method on human action datasets and compare with related methods. It is shown that the proposed method achieves promising performance and improves the efficiency.