Learning the discriminative dictionary for sparse representation by a general fisher regularized model

Qingfeng Liu; Ajit Puthenputhussery; Chengjun Liu

doi:10.1109/ICIP.2015.7351627

Learning the discriminative dictionary for sparse representation by a general fisher regularized model

Qingfeng Liu, Ajit Puthenputhussery, Chengjun Liu

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This paper presents two novel discriminative dictionary learning models for sparse representation, namely the Fisher discriminative sparse model (FDSM) and the marginal Fisher discriminative sparse model (MFDSM). To learn the FDSM and the MFDSM efficiently and homogeneously, a general Fisher regularized model is further derived so that both of them can be learned without much modification. Experimental results on four popular databases, namely the extended Yale face database B, the AR face database, the 15 scenes dataset and the MIT-67 indoor scenes dataset show that the proposed method can improve upon other popular methods.

Original language	English (US)
Title of host publication	2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
Publisher	IEEE Computer Society
Pages	4347-4351
Number of pages	5
Volume	2015-December
ISBN (Electronic)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7351627
State	Published - Dec 9 2015
Event	IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: Sep 27 2015 → Sep 30 2015

Other

Other	IEEE International Conference on Image Processing, ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	9/27/15 → 9/30/15

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2015.7351627

Cite this

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title = "Learning the discriminative dictionary for sparse representation by a general fisher regularized model",

abstract = "This paper presents two novel discriminative dictionary learning models for sparse representation, namely the Fisher discriminative sparse model (FDSM) and the marginal Fisher discriminative sparse model (MFDSM). To learn the FDSM and the MFDSM efficiently and homogeneously, a general Fisher regularized model is further derived so that both of them can be learned without much modification. Experimental results on four popular databases, namely the extended Yale face database B, the AR face database, the 15 scenes dataset and the MIT-67 indoor scenes dataset show that the proposed method can improve upon other popular methods.",

author = "Qingfeng Liu and Ajit Puthenputhussery and Chengjun Liu",

year = "2015",

month = dec,

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doi = "10.1109/ICIP.2015.7351627",

language = "English (US)",

volume = "2015-December",

pages = "4347--4351",

booktitle = "2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings",

publisher = "IEEE Computer Society",

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note = "IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

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Liu, Q, Puthenputhussery, A & Liu, C 2015, Learning the discriminative dictionary for sparse representation by a general fisher regularized model. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. vol. 2015-December, 7351627, IEEE Computer Society, pp. 4347-4351, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 9/27/15. https://doi.org/10.1109/ICIP.2015.7351627

Learning the discriminative dictionary for sparse representation by a general fisher regularized model. / Liu, Qingfeng; Puthenputhussery, Ajit; Liu, Chengjun.

2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. Vol. 2015-December IEEE Computer Society, 2015. p. 4347-4351 7351627.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Learning the discriminative dictionary for sparse representation by a general fisher regularized model

AU - Liu, Qingfeng

AU - Puthenputhussery, Ajit

AU - Liu, Chengjun

PY - 2015/12/9

Y1 - 2015/12/9

N2 - This paper presents two novel discriminative dictionary learning models for sparse representation, namely the Fisher discriminative sparse model (FDSM) and the marginal Fisher discriminative sparse model (MFDSM). To learn the FDSM and the MFDSM efficiently and homogeneously, a general Fisher regularized model is further derived so that both of them can be learned without much modification. Experimental results on four popular databases, namely the extended Yale face database B, the AR face database, the 15 scenes dataset and the MIT-67 indoor scenes dataset show that the proposed method can improve upon other popular methods.

AB - This paper presents two novel discriminative dictionary learning models for sparse representation, namely the Fisher discriminative sparse model (FDSM) and the marginal Fisher discriminative sparse model (MFDSM). To learn the FDSM and the MFDSM efficiently and homogeneously, a general Fisher regularized model is further derived so that both of them can be learned without much modification. Experimental results on four popular databases, namely the extended Yale face database B, the AR face database, the 15 scenes dataset and the MIT-67 indoor scenes dataset show that the proposed method can improve upon other popular methods.

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M3 - Conference contribution

AN - SCOPUS:84956678355

VL - 2015-December

SP - 4347

EP - 4351

BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings

PB - IEEE Computer Society

T2 - IEEE International Conference on Image Processing, ICIP 2015

Y2 - 27 September 2015 through 30 September 2015

ER -

Learning the discriminative dictionary for sparse representation by a general fisher regularized model

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