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) |
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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 | |
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 |
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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