%0 Journal Article
%T A Generalized Principal Component Analysis Based on Image Matrix<br>基于图像矩阵的广义主分量分析
%A Chen Cai-kou
%A Yang Jian
%A Yang Jing-yu
%A Gao Xiu-mei
%A <br>陈才扣
%A 杨健
%A 杨静宇
%A 高秀梅
%J 电子与信息学报
%D 2004
%I 
%X The classical Principal Component Analysis (PCA) for image feature extraction is usually based on vectors, which makes it very time-consuming, and the class information in the training sample has not been utilized fully also. To overcome these two drawbacks of PCA, this paper proposes a novel and efficient PCA method based on original image matrices directly. It can extract the discriminant information included in the class mean images. Hence, the proposed method has better discriminant performance than classical PCA. Experimental results on ORL face database show the proposed method is more powerful and efficient than the classical PCA and Fisher linear discriminant analysis.
%K Image recognition
%K Principal Component Analysis (PCA)
%K Image matrix
%K Feature extraction<br>图像识别
%K 主分量分析
%K 图像矩阵
%K 特征抽取
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=1724488FC8663B2F&yid=D0E58B75BFD8E51C&vid=96C778EE049EE47D&iid=59906B3B2830C2C5&sid=65B74213B7DFEAD0&eid=11C933E0BC2B8917&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=1&reference_num=7