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Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification

International journal of computer vision, 2014-09, Vol.109 (3), p.209-232 [Peer Reviewed Journal]

Springer Science+Business Media New York 2014 ;2015 INIST-CNRS ;COPYRIGHT 2014 Springer ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-014-0722-8

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Title:
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification
Author: Yang, Meng ; Zhang, Lei ; Feng, Xiangchu ; Zhang, David
Subjects: Analysis ; Applied sciences ; Artificial Intelligence ; Classification ; Coding, codes ; Computer Imaging ; Computer Science ; Computer science; control theory; systems ; Detection, estimation, filtering, equalization, prediction ; Dictionaries ; Discrimination ; Exact sciences and technology ; Image classification ; Image Processing and Computer Vision ; Information, signal and communications theory ; Learning ; Learning and adaptive systems ; Machine learning ; Mathematical models ; Pattern Recognition ; Pattern Recognition and Graphics ; Pattern recognition. Digital image processing. Computational geometry ; Principal components analysis ; Representations ; Signal and communications theory ; Signal, noise ; Sparsity ; Studies ; Tasks ; Teaching methods ; Telecommunications and information theory ; Vision
Is Part Of: International journal of computer vision, 2014-09, Vol.109 (3), p.209-232
Description: 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.
Publisher: Boston: Springer US
Language: English
Identifier: ISSN: 0920-5691
EISSN: 1573-1405
DOI: 10.1007/s11263-014-0722-8
Source: AUTh Library subscriptions: ProQuest Central
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Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification

Springer Science+Business Media New York 2014 ;2015 INIST-CNRS ;COPYRIGHT 2014 Springer ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-014-0722-8

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