Fisher Discrimination Dictionary Learning for sparse representation

Meng Yang, Lei Zhang, Xiangchu Feng, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

831 Citations (Scopus)


Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.
Original languageEnglish
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Number of pages8
Publication statusPublished - 1 Dec 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011


Conference2011 IEEE International Conference on Computer Vision, ICCV 2011

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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