Using discriminant eigenfeatures for image retrieval

被引：1043

作者：

Swets, DL ^{[1
]}

Weng, JJ ^{[1
]}

机构：

[1] MICHIGAN STATE UNIV, DEPT COMP SCI, E LANSING, MI 48824 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1996年 / 18卷 / 08期

基金：

美国国家科学基金会;

关键词：

principal component analysis; discriminant analysis; eigenfeature; image retrieval; feature selection; face recognition; object recognition; content-based image retrieval;

D O I：

10.1109/34.531802

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class retrieval from a large database of widely varying real-world objects presented as ''well-framed'' views, and compare it with that of the principal component analysis.

引用

页码：831 / 836

页数：6

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