Linear discriminative image processing operator analysis

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.

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Toru Tamaki, Bingzhi Yuan, Kengo Harada, Bisser Raytchev, Kazufumi Kaneda
• Keywords: Computer Vision, Cvpr 2012, Feature Space, Iterative Method, Linear Discriminant Analysis