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Combining unsupervised learning and discrimination for 3D action recognition

Chen, Guang; Clarke, Daniel; Giuliani, Manuel; Gaschler, Andre; Knoll, Alois


Guang Chen

Daniel Clarke

Manuel Giuliani
Co- Director Bristol Robotics Laboratory

Andre Gaschler

Alois Knoll


© 2014 Elsevier B.V. Previous work on 3D action recognition has focused on using hand-designed features, either from depth videos or 2D videos. In this work, we present an effective way to combine unsupervised feature learning with discriminative feature mining. Unsupervised feature learning allows us to extract spatio-temporal features from unlabeled video data. With this, we can avoid the cumbersome process of designing feature extraction by hand. We propose an ensemble approach using a discriminative learning algorithm, where each base learner is a discriminative multi-kernel-learning classifier, trained to learn an optimal combination of joint-based features. Our evaluation includes a comparison to state-of-the-art methods on the MSRAction 3D dataset, where our method, abbreviated EnMkl, outperforms earlier methods. Furthermore, we analyze the efficiency of our approach in a 3D action recognition system.


Chen, G., Clarke, D., Giuliani, M., Gaschler, A., & Knoll, A. (2015). Combining unsupervised learning and discrimination for 3D action recognition. Signal Processing, 110, 67-81.

Journal Article Type Article
Acceptance Date Aug 16, 2014
Online Publication Date Aug 23, 2014
Publication Date 2015-05
Journal Signal Processing
Print ISSN 0165-1684
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 110
Pages 67-81
Keywords human action recognition, depth camera, unsupervised learning, multi-kernel learning, ensemble learning
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