Coherent and Noncoherent Dictionaries for Action Recognition

Wilson, S and C, Krishna Mohan (2017) Coherent and Noncoherent Dictionaries for Action Recognition. IEEE Signal Processing Letters, 24 (5). pp. 698-702. ISSN 1070-9908

Full text not available from this repository. (Request a copy)


In this letter, we propose sparsity-based coherent and noncoherent dictionaries for action recognition. First, the input data are divided into different clusters and the number of clusters depends on the number of action categories. Within each cluster, we seek data items of each action category. If the number of data items exceeds threshold in any action category, these items are labeled as coherent. In a similar way, all coherent data items from different clusters form a coherent group of each action category, and data that are not part of the coherent group belong to noncoherent group of each action category. These coherent and noncoherent groups are learned using K-singular value decomposition dictionary learning. Since the coherent group has more similarity among data, only few atoms need to be learned. In the noncoherent group, there is a high variability among the data items. So, we propose an orthogonal-projection-based selection to get optimal dictionary in order to retain maximum variance in the data. Finally, the obtained dictionary atoms of both groups in each action category are combined and then updated using the limited Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The experiments are conducted on challenging datasets HMDB51 and UCF50 with action bank features and achieve comparable result using this state-of-the-art feature.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Coherency; dictionary learning; sparse coding
Subjects: Electrical Engineering > Electrical and Electronic
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 07 Jun 2017 04:28
Last Modified: 01 Sep 2017 09:00
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 3201 Statistics for this ePrint Item