Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17910
Title: Fisher Discrimination Dictionary Learning for sparse representation
Authors: Yang, M
Zhang, L 
Feng, X
Zhang, D 
Issue Date: 2011
Source: Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 543-550 How to cite?
Abstract: 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.
Description: 2011 IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, 6-13 November 2011
URI: http://hdl.handle.net/10397/17910
ISBN: 9781457711015
DOI: 10.1109/ICCV.2011.6126286
Appears in Collections:Conference Paper

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