Abstract
Activity recognition is a complex problem mainly because of the nature of the data. Data usually are high dimensional, so applying a classifier directly to the data is not always a good practice. A common method is to find a meaningful representation of complex data through dimensionality reduction. In this paper we propose novel kernel matrices based on graph theory to be used for dimensionality reduction. The proposed kernel can be embedded in a general dimensionality reduction framework. Experiments on a traditional dance recognition dataset are conducted and the advantage of using dimensionality reduction before classification is highlighted.
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References
Chapelle, O., Weston, J., Scholkopf, B.: Cluster kernels for semi-supervised learning. In: Advances in Neural Information Processing Systems, p. 15 (2002)
Chung, F.R.K.: Spectral Graph Theory (CBMS Regional Conference Series in Mathematics, No. 92). American Mathematical Society (December 1996)
He, X., Niyogi, P.: Locality preserving projections (2002)
Iosifidis, A., Tefas, A., Pitas, I.: View-invariant action recognition based on artificial neural networks. IEEE Transactions on Neural Networks and Learning Systems 23(3), 412–424 (2012)
Ji, X., Liu, H.: Advances in view-invariant human motion analysis: A review. Trans. Sys. Man Cyber. Part C 40(1), 13–24 (2010)
Kapsouras, I., Karanikolos, S., Nikolaidis, N., Tefas, A.: Feature comparison and feature fusion for traditional dances recognition. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part I. CCIS, vol. 383, pp. 172–181. Springer, Heidelberg (2013)
Kyperountas, M., Tefas, A., Pitas, I.: Salient feature and reliable classifier selection for facial expression classification. Pattern Recognition 43(3), 972–986 (2010)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Le, Q.V., Zou, W.Y., Yeung, S.Y., Ng, A.Y.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 3361–3368. IEEE Computer Society, Washington, DC (2011)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)
Poppe, R.: A survey on vision-based human action recognition. Image Vision Comput. 28(6), 976–990 (2010)
Smola, A.J., Kondor, R.: Kernels and regularization on graphs (2003)
Szummer, M., Jaakkola, T.: Partially labeled classification with markov random walks. In: Advances in Neural Information Processing Systems 15. MIT Press, Cambridge (2001)
Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision 103(1), 60–79 (2013)
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)
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Gavriilidis, V., Tefas, A. (2014). Activity Recognition for Traditional Dances Using Dimensionality Reduction. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_10
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DOI: https://doi.org/10.1007/978-3-319-07064-3_10
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