Domain adaptive dictionary learning

Qiang Qiu, Vishal M. Patel, Pavan Turaga, Rama Chellappa

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

55 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages631-645
Number of pages15
EditionPART 4
DOIs
StatePublished - Oct 30 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7575 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th European Conference on Computer Vision, ECCV 2012
CountryItaly
CityFlorence
Period10/7/1210/13/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Qiu, Q., Patel, V. M., Turaga, P., & Chellappa, R. (2012). Domain adaptive dictionary learning. In Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings (PART 4 ed., pp. 631-645). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7575 LNCS, No. PART 4). https://doi.org/10.1007/978-3-642-33765-9_45