From image vector to matrix: a straightforward image projection technique - IMPCA vs. PCA

被引：154
|
作者：
Yang, J [1 ]
Yang, JY [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R China
来源：
PATTERN RECOGNITION | 2002年 / 35卷 / 09期
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1016/S0031-3203(02)00040-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and efficient than conventional PCA and FLD. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：1997 / 1999
页数：3
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