Fast sparse representation with prototypes

Jia Bin Huang, Ming Hsuan Yang

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

37 Citations (Scopus)

Abstract

Sparse representation has found applications in numerous domains and recent developments have been focused on the convex relaxation of the ℓ0-norm minimization for sparse coding (i.e., the ℓ1-norm minimization). Nevertheless, the time and space complexities of these algorithms remain significantly high for large-scale problems. As signals in most problems can be modeled by a small set of prototypes, we propose an algorithm that exploits this property and show that the ℓ1-norm minimization problem can be reduced to a much smaller problem, thereby gaining significant speed-ups with much less memory requirements. Experimental results demonstrate that our algorithm is able to achieve double-digit gain in speed with much less memory requirement than the state-of-the-art algorithms.

Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages3618-3625
Number of pages8
DOIs
Publication statusPublished - 2010 Aug 31
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: 2010 Jun 132010 Jun 18

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
CountryUnited States
CitySan Francisco, CA
Period10/6/1310/6/18

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Huang, J. B., & Yang, M. H. (2010). Fast sparse representation with prototypes. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 (pp. 3618-3625). [5539919] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2010.5539919