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Cascade dictionary learning for action recognition

  • Southeast University, Nanjing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we propose a cascade dictionary learning algorithm for action recognition. In the first stage, a dictionary for basic sparse coding is learned based on local descriptors. And then spatial pyramid features are extracted to represent all the images in the same dimensions. Instead of performing dimension reduction, all the features are regrouped and then fed into second dictionary learning. In the second stage, a supervised dictionary for block and group sparse coding is learned to get discriminative representations based on the regrouped features. Without lowering classification performance, the size of the second dictionary is much smaller than other dictionary based on spatial pyramid features. We evaluate our algorithm on two publicly available databases about action recognition: Willows and People Playing Music Instrument. The numerical results show the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIMSIVP 2014
Subtitle of host publication2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479945047
DOIs
StatePublished - 16 Jan 2015
Externally publishedYes
Event2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, CIMSIVP 2014 - Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Publication series

NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIMSIVP 2014: 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Proceedings

Conference

Conference2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, CIMSIVP 2014
Country/TerritoryUnited States
CityOrlando
Period9/12/1412/12/14

Keywords

  • block and group sparse coding
  • cascade dictionary learning
  • feature regrouping
  • spatial pyramid

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