Domain adaptive dictionary learning

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

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

49 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages631-645
Number of pages15
Volume7575 LNCS
EditionPART 4
DOIs
StatePublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Glossaries
Invariant
Pose Estimation
Sparse Representation
Face recognition
Face Recognition
Computer Vision
Computer vision
Illumination
Alignment
Lighting
Dictionary
Learning
Optimization Problem
Testing
Demonstrate
Experiment
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Qiu, Q., Patel, V. M., Turaga, P., & Chellappa, R. (2012). Domain adaptive dictionary learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 4 ed., Vol. 7575 LNCS, 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

Domain adaptive dictionary learning. / Qiu, Qiang; Patel, Vishal M.; Turaga, Pavan; Chellappa, Rama.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7575 LNCS PART 4. ed. 2012. p. 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).

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

Qiu, Q, Patel, VM, Turaga, P & Chellappa, R 2012, Domain adaptive dictionary learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 4 edn, vol. 7575 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 4, vol. 7575 LNCS, pp. 631-645, 12th European Conference on Computer Vision, ECCV 2012, Florence, Italy, 10/7/12. https://doi.org/10.1007/978-3-642-33765-9_45
Qiu Q, Patel VM, Turaga P, Chellappa R. Domain adaptive dictionary learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 4 ed. Vol. 7575 LNCS. 2012. p. 631-645. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4). https://doi.org/10.1007/978-3-642-33765-9_45
Qiu, Qiang ; Patel, Vishal M. ; Turaga, Pavan ; Chellappa, Rama. / Domain adaptive dictionary learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7575 LNCS PART 4. ed. 2012. pp. 631-645 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4).
@inproceedings{11247927436647ac97fe8f077041561c,
title = "Domain adaptive dictionary learning",
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.",
author = "Qiang Qiu and Patel, {Vishal M.} and Pavan Turaga and Rama Chellappa",
year = "2012",
doi = "10.1007/978-3-642-33765-9_45",
language = "English (US)",
isbn = "9783642337642",
volume = "7575 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 4",
pages = "631--645",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 4",

}

TY - GEN

T1 - Domain adaptive dictionary learning

AU - Qiu, Qiang

AU - Patel, Vishal M.

AU - Turaga, Pavan

AU - Chellappa, Rama

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84867868851&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867868851&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-33765-9_45

DO - 10.1007/978-3-642-33765-9_45

M3 - Conference contribution

SN - 9783642337642

VL - 7575 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 631

EP - 645

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -