Open AccessShift Invariant Dictionary Learning for Human Action Recognition
Author(s) -
Ushapreethi P*,
Lakshmi Priya G G
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7005.129219
Subject(s) - dictionary learning , computer science , k svd , invariant (physics) , sparse approximation , pattern recognition (psychology) , artificial intelligence , representation (politics) , action recognition , speech recognition , machine learning , mathematics , politics , political science , law , mathematical physics , class (philosophy)
Sparse representation is an emerging topic among researchers. The method to represent the huge volume of dense data as sparse data is much needed for various fields such as classification, compression and signal denoising. The base of the sparse representation is dictionary learning. In most of the dictionary learning approaches, the dictionary is learnt based on the input training signals which consumes more time. To solve this issue, the shift-invariant dictionary is used for action recognition in this work. Shift-Invariant Dictionary (SID) is that the dictionary is constructed in the initial stage with shift-invariance of initial atoms. The advantage of the proposed SID based action recognition method is that it requires minimum training time and achieves highest accuracy.