Accession Number:

ADA585819

Title:

Multi-observation Visual Recognition via Joint Dynamic Sparse Representation

Descriptive Note:

Conference paper

Corporate Author:

RICE UNIV HOUSTON TX OFFICE OF SPONSORED RESEARCH

Report Date:

2011-11-01

Pagination or Media Count:

10.0

Abstract:

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.

Subject Categories:

  • Anatomy and Physiology
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE