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Efficient Local Feature Encoding for Human Action Recognition with approximate sparse coding

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2016年第4期E99D卷 1212-1220页

作者： Wang, Yu Kato, Jien Nagoya Univ Grad Sch Informat Sci Nagoya Aichi 4648603 Japan

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called approximate sparse coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using sparse coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

关键词： approximate sparse coding sparse coding approximate nearest neighbour local feature encoding action recognition

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Action recognition with approximate sparse coding

Action recognition with approximate sparse coding

引用

IEEE International Conference on Image Processing, ICIP 2015

作者： Wang, Yu Kato, Jien Graduate School of Information Science Nagoya University Japan

ISBN: (纸本)9781479983391

In this paper, we present a novel feature encoding approach called approximate sparse coding (ASC). ASC computes the sparse codes for a large collection of prototype descriptors in the off-line learning phase with sparse coding (SC);and look up the nearest prototype's sparse code for each to-be-encoded descriptor in the encoding phase with approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the fast speed of ANN, which are both desired properties for the human action recognition task. We excessively evaluated ASC on the popular HMDB51 dataset, and confirme it is able to encode large number of video features into discriminative low dimensional representations efficientl. © 2015 IEEE.

关键词： action recognition approximate nearest neighbour approximate sparse coding sparse coding

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ACTION RECOGNITION WITH approximate sparse coding

ACTION RECOGNITION WITH APPROXIMATE SPARSE CODING

引用

IEEE International Conference on Image Processing

作者： Yu Wang Jien Kato Graduate School of Information Science Nagoya University

ISBN: (纸本)9781479983407

关键词： Action recognition approximate sparse coding sparse coding approximate nearest neighbour sparse coding fast speed PYCARD gene Automatic System Control Artificial neural networks Scandium

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