The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Semi-latent Dirichlet allocation: a hierarchical model for human action recognition
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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The method based on local features has an advantage that the important local motion feature is represented as bag-of-features, but lacks the location information. Additionally, in order to employ an approach based on bag-of-features, language models represented by pLSA and LDA (Latent Dirichlet Allocation) have to be applied to. These are unsupervised learning, but they require the number of latent topics to be set manually. In this study, in order to perform the LDA without specifying the number of the latent topics, and also to deal with multiple words concurrently, we propose unsupervised Multiple Instances Hierarchical Dirichlet Process MI-HDP-LDA by employing the local information concurrently. The proposed method, unsupervised MI-HDP-LDA, was evaluated for Weizmann dataset. The average recognition rate by LDA as conventional method was 61.8% and by the proposed method it was 73.7%, resulting in 11.9 points improvement.