NDLI: Action Recognition by Hierarchical Sequence Summarization

Content Provider	IEEE Xplore Digital Library
Author	Yale Song Morency, L.-P. Davis, R.
Copyright Year	2013
Abstract	Recent progress has shown that learning from hierarchical feature representations leads to improvements in various computer vision tasks. Motivated by the observation that human activity data contains information at various temporal resolutions, we present a hierarchical sequence summarization approach for action recognition that learns multiple layers of discriminative feature representations at different temporal granularities. We build up a hierarchy dynamically and recursively by alternating sequence learning and sequence summarization. For sequence learning we use CRFs with latent variables to learn hidden spatio-temporal dynamics, for sequence summarization we group observations that have similar semantic meaning in the latent space. For each layer we learn an abstract feature representation through non-linear gate functions. This procedure is repeated to obtain a hierarchical sequence summary representation. We develop an efficient learning method to train our model and show that its complexity grows sub linearly with the size of the hierarchy. Experimental results show the effectiveness of our approach, achieving the best published results on the Arm Gesture and Canal9 datasets.
Starting Page	3562
Ending Page	3569
File Size	674173
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.457
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logic gates Complexity theory Training Optimization Mathematical model Abstracts Semantics Conditional Random Fields Action Recognition Hierarchical Model
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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