On improvement of feature extraction algorithms for discriminative pattern classification

Author

Gao, Jiang ; Ding, Xiaoqing

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing, China

Volume

2

fYear

2000

fDate

2000

Firstpage

101

Abstract

Two new feature extraction strategies-modified multiple discriminant analysis (MMDA) and difference principal component analysis (DPCA)-are presented and derived. The proposed algorithms are especially useful in automatic feature extraction from patterns in a small category set. Experimental results for recognition of Chinese character fonts and handwritten numerals using MMDA and DPCA are presented. Compared with the traditional algorithms, MMDA and DPCA provide more effective feature metrics for pattern discrimination in some settings

Keywords

character recognition; covariance matrices; feature extraction; pattern classification; principal component analysis; Chinese character fonts; automatic feature extraction; difference principal component analysis; discriminative pattern classification; feature extraction algorithms; feature metrics; handwritten numerals; modified multiple discriminant analysis; Character recognition; Covariance matrix; Eigenvalues and eigenfunctions; Feature extraction; Handwriting recognition; Humans; Image processing; Pattern classification; Principal component analysis; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.906026

Filename

906026