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Domain Adaptive Dictionary Learning

  • Qiang Qiu
  • Vishal M. Patel
  • Pavan Turaga
  • Rama Chellappa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

Many recent efforts have shown the effectiveness of dictionary learning methods in solving several computer vision problems. However, when designing dictionaries, training and testing domains may be different, due to different view points and illumination conditions. In this paper, we present a function learning framework for the task of transforming a dictionary learned from one visual domain to the other, while maintaining a domain-invariant sparse representation of a signal. Domain dictionaries are modeled by a linear or non-linear parametric function. The dictionary function parameters and domain-invariant sparse codes are then jointly learned by solving an optimization problem. Experiments on real datasets demonstrate the effectiveness of our approach for applications such as face recognition, pose alignment and pose estimation.

Keywords

Face Recognition Source Image Sparse Representation Sparse Code Domain Parameter 
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 2012

Authors and Affiliations

  • Qiang Qiu
    • 1
  • Vishal M. Patel
    • 1
  • Pavan Turaga
    • 2
  • Rama Chellappa
    • 1
  1. 1.Center for Automation ResearchUMIACS, University of MarylandCollege ParkUSA
  2. 2.Arts Media and EngineeringArizona State UniversityUSA

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