Multi-observation visual recognition via joint dynamic sparse representation

Haichao Zhang, Nasser M. Nasrabadi, Zhang Yanning Zhang, Thomas S Huang

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


We address the problem of visual recognition from multiple observations of the same physical object, which can be generated under different conditions, such as frames at different time instances or snapshots from different viewpoints. We formulate the multi-observation visual recognition task as a joint sparse representation model and take advantage of the correlations among the multiple observations for classification using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity pattern among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class in order to facilitate a flexible representation. The proposed method can handle both homogenous as well as heterogenous data within the same framework. Extensive experiments on various visual classification tasks including face recognition and generic object classification demonstrate that the proposed method outperforms existing state-of-the-art methods

Original languageEnglish (US)
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Number of pages8
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other2011 IEEE International Conference on Computer Vision, ICCV 2011

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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