A Fourier-LDA approach for image recognition

被引：36

作者：

Jing, XY

Tang, YY

Zhang, D ^{[1
]}

机构：

[1] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China

[2] Harbin Inst Technol, Biocomp Res Ctr, Shenzhen, Guangdong Prov, Peoples R China

[3] Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen, Guangdong Prov, Peoples R China

[4] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 03期

关键词：

Fourier transform; linear discrimination analysis (LDA); two-dimensional separability judgment; frequency-band selection; Fourier-LDA approach (FLA);

D O I：

10.1016/j.patcog.2003.09.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fourier transform and linear discrimination analysis (LDA) are two commonly used techniques of image processing and recognition. Based on them, we propose a Fourier-LDA approach (FLA) for image recognition. It selects appropriate Fourier frequency bands with favorable linear separability by using a two-dimensional separability judgment. Then it extracts two-dimensional linear discriminative features to perform the classification. Our experimental results on different image data prove that FLA obtains better classification performance than other linear discrimination methods. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：453 / 457

页数：5

共 6 条

[1] Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J].