Linear Discriminative Image Processing Operator Analysis

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2012) Page 2526-2532 published_at 2012
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Title ( eng )
Linear Discriminative Image Processing Operator Analysis
Creator
Yuan Bingzhi
Harada Kengo
Raytchev Bisser
Source Title
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2012)
Start Page 2526
End Page 2532
Abstract
In this paper, we propose a method to select a discriminative set of image processing operations for Linear Discriminant Analysis (LDA) as an application of the use of generating matrices representing image processing operators acting on images. First we show that generating matrices can be used for formulating LDA with increasing training samples, then analyze them as image processing operators acting on 2D continuous functions for compressing many large generating matrices by using PCA and Hermite decomposition. Then we propose Linear Discriminative Image Processing Operator Analysis, an iterative method for estimating LDA feature space along with a discriminative set of generating matrices. In experiments, we demonstrate that discriminative generating matrices outperform a nondiscriminative set on the ORL and FERET datasets.
NDC
Electrical engineering [ 540 ]
Language
eng
Resource Type conference paper
Publisher
IEEE
Date of Issued 2012
Rights
Copyright (c) 2012 IEEE
Publish Type Author’s Original
Access Rights open access
Source Identifier
[ISSN] 1063-6919
[URI] http://ir.lib.hiroshima-u.ac.jp/00033094