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

Qingfeng Liu, Ajit Puthenputhussery, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages4347-4351
Number of pages5
Volume2015-December
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period9/27/159/30/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint Dive into the research topics of 'Learning the discriminative dictionary for sparse representation by a general fisher regularized model'. Together they form a unique fingerprint.

Cite this