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Joint learning and dictionary construction for pattern recognition

Pham, Duc-Son and Venkatesh, Svetha 2008, Joint learning and dictionary construction for pattern recognition, in CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-8.

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Title Joint learning and dictionary construction for pattern recognition
Author(s) Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 23-28 Jun. 2008
Title of proceedings CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2008
Conference series IEEE Conference on Computer Vision and Pattern Recognition
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) computer vision
constraint optimization
dictionaries
encoding
face recognition
feature extraction
image coding
image databases
image processing
pattern recognition
Summary We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.
ISBN 1424422426
9781424422425
ISSN 1063-6919
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044578

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.