Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35953
Title: Sparse representation based fisher discrimination dictionary learning for image classification
Authors: Yang, M
Zhang, L 
Feng, XC
Zhang, D 
Keywords: Dictionary learning
Sparse representation
Fisher criterion
Image classification
Issue Date: 2014
Publisher: Springer
Source: International journal of computer vision, 2014, v. 109, no. 3, p. 209-232 How to cite?
Journal: International journal of computer vision 
Abstract: The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.
URI: http://hdl.handle.net/10397/35953
ISSN: 0920-5691 (print)
1573-1405 (online)
DOI: 10.1007/s11263-014-0722-8
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