Sparsifying Dense Features for Action Classification

Roy, D and M, Srinivasa and C, Krishna Mohan (2015) Sparsifying Dense Features for Action Classification. In: Proceedings of the 2nd International Conference on Perception and Machine Intelligence (PerMIn), 26-27, February 2015, Kolkata, West Bengal.

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We propose an approach for sparse representation of dense features for action classification. Sparse representation has already been shown in literature as a good approximation for signals for various computer vision applications. This property is leveraged to represent a dense feature like action bank in the form of sparse dictionaries. These dictionaries are learnt using on-line dictionary learning (ODL) which further facilitates incorporating new training examples into existing dictionaries for more robust representation of various categories of action as and when required. Evaluation of the proposed method on realistic action datasets like UCF50 and HMDB51 shows that considering sparse representation of a dense feature is more suitable for classification than the feature itself.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Action Recognition, Dictionary Learning, Sparse Representation
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 12 Feb 2016 04:35
Last Modified: 01 Sep 2017 09:21
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