PCA and LDA in DCT domain

被引：82

作者：

Chen, WL ^{[1
]}

Er, MJ ^{[1
]}

Wu, SQ ^{[1
]}

机构：

[1] Nanyang Technol Univ, Intelligent Syst Ctr, Singapore 637533, Singapore

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 15期

关键词：

principal component analysis; linear discriminant analysis; discrete cosine transform; face recognition;

D O I：

10.1016/j.patrec.2005.05.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement. For images compressed using the DCT, e.g., in JPEG or MPEG standard, the PCA and LDA can be directly implemented in the DCT domain such that the inverse DCT transform can be skipped and the dimensionality of the original data can be initially reduced to cut down computational cost. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：2474 / 2482

页数：9