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Domain Adaptive Dictionary Learning
Qiang Qiu1, Vishal M. Patel1, Pavan Turaga2, and Rama Chellappa1
1Center for Automation Research, UMIACS, University of Maryland, College Park, USA
qiu@cs.umd.edu
pvishalm@umiacs.umd.edu
rama@umiacs.umd.edu
2Arts Media and Engineering, Arizona State University, USA
pturaga@asu.edu
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.
LNCS 7575, p. 631 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012