NDLI: Learning directional co-occurrence for human action classification

Content Provider	IEEE Xplore Digital Library
Author	Hong Liu Mengyuan Liu Qianru Sun
Copyright Year	2014
Description	Author affiliation: Key Lab. of Machine Perception, Peking Univ., Beijing, China (Hong Liu; Mengyuan Liu; Qianru Sun)
Abstract	Spatio-temporal interest point (STIP) based methods have shown promising results for human action classification. However, state-of-art works typically utilize bag-of-visual words (BoVW), which focuses on the statistical distribution of features but ignores their inherent structural relationships. To solve this problem, a descriptor, namely directional pair-wise feature (DPF), is proposed to encode the mutual direction information between pairwise words, aiming at adding more spatial discriminant to BoVW. Firstly, STIP features are extracted and classified into a set of labeled words. Then in each frame, the DPF is constructed for every pair of words with different labels, according to their assigned directional vector. Finally, DPFs are quantized to be a probability histogram as a representation of human action. The proposed method is evaluated on two challenging datasets, Rochester and UT-interaction, and the results based on chi-squared kernel SVM classifiers confirm that our method can classify human actions with high accuracies.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	1235
Ending Page	1239
File Size	315874
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6853794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Streaming media Sun Feature extraction Clustering methods Visualization co-occurrence Spatio-temporal interest point bag-of-visual words
Content Type	Text
Resource Type	Article

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