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Tensor Sparse Coding for Region Covariances

  • Ravishankar Sivalingam
  • Daniel Boley
  • Vassilios Morellas
  • Nikolaos Papanikolopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Sparse representation of signals has been the focus of much research in the recent years. A vast majority of existing algorithms deal with vectors, and higher–order data like images are usually vectorized before processing. However, the structure of the data may be lost in the process, leading to poor representation and overall performance degradation. In this paper we propose a novel approach for sparse representation of positive definite matrices, where vectorization would have destroyed the inherent structure of the data. The sparse decomposition of a positive definite matrix is formulated as a convex optimization problem, which falls under the category of determinant maximization (MAXDET) problems [1], for which efficient interior point algorithms exist. Experimental results are shown with simulated examples as well as in real–world computer vision applications, demonstrating the suitability of the new model. This forms the first step toward extending the cornucopia of sparsity-based algorithms to positive definite matrices.

Keywords

Face Recognition Sparse Representation Sparse Code Region Covariance Pedestrian Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ravishankar Sivalingam
    • 1
  • Daniel Boley
    • 1
  • Vassilios Morellas
    • 1
  • Nikolaos Papanikolopoulos
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of MinnesotaMinneapolisUSA

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