Cross-view action recognition via low-rank based domain adaptation

Wen Sheng Tseng, Lun Kai Hsu, Li Wei Kang, Yu Chiang Frank Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cross-view action recognition is a challenging problem, since one typically does not have sufficient training data at the target view of interest. With recent developments of domain adaptation, we propose a novel low-rank based domain adaptation model for mapping labeled data from the original source view to the target view, so that training and testing can be performed at that domain. Our model not only provides an effective way for associating image data across different domains, we further advocate the structural incoherence between transformed data of different categories. As a result, additional data discriminating ability is introduced to our domain adaptation model, and thus improved recognition can be expected. Experimental results on the IXMAS dataset verify the effectiveness of our proposed method, which is shown to outperform state-of-the-art domain adaptation approaches.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3244-3248
Number of pages5
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period2013/09/152013/09/18

Keywords

  • Action recognition
  • domain adaptation
  • low-rank matrix decomposition

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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