NDLI: Human action recognition using modified slow feature analysis and multiple kernel learning

Content Provider	Springer Nature Link
Author	Xiao, Yongliang Xia, Limin
Copyright Year	2015
Abstract	A novel human action recognition method is proposed, which includes two periods of action feature extraction and action recognition. Firstly, we use a modified slow feature analysis (SFA) to extract video local feature. Unlike slow feature analysis, we redefine the objective function with supervised information, which make the modified SFA more suitable to preserve the slow feature and label information. Meanwhile, in effort to cope with the dimension explosion in SFA, locality preserving projections (LPP) is used to reduce the quadratic expansion dimension. Secondly, we use a multiple kernel learning method (MKL) to classify human action, in which the weights of different kernels are optimized by combining Bacterial Chemotaxis method and Powell method. The results of experiments indicate the efficiency of our method.
Starting Page	13041
Ending Page	13056
Page Count	16
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	75
Issue Number	21
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2015-04-02
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Human action recognition Slow feature analysis Multiple kernel learning Powell method Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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