Abstract
Human action sequences can be considered as nonlinear dynamic manifolds in image frames space. In this paper, a novel manifold embedding method, Maximum Temporal Inter-class Dissimilarity (MTID), is proposed for human action recognition, which is based on the framework of Locality Preserving Projections (LPP). Being different from LPP whose goal is to minimize the intra-class distance in local neighborhood, MTID can make best of both the class label information and the temporal information to maximize the inter-class distance in local neighborhood, Namely, focusing on maximizing the dissimilarity between frames that are similar in appearance but are from different classes. At last the Nearest Neighbors classifier based on Hausdorff distance is introduced for recognition. The experimental results demonstrate the effectiveness of the proposed method for human action recognition.
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Notes
- 1.
t is a temporal segmentation parameter like in LSTDE [1].
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Acknowledgements
This research is supported by National Natural Science Foundation of China (61201271), Specialized Research Fund for the Doctoral Program of Higher Education (20100185120021), and Sichuan science and technology support program (cooperated with Chinese Academy of Sciences) (2012JZ0001).
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Liu, H., Li, L. (2014). Human Action Recognition Using Maximum Temporal Inter-Class Dissimilarity. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_111
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DOI: https://doi.org/10.1007/978-3-319-00536-2_111
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