Optimal linear representations of images for object recognition

被引：67

作者：

Liu, XW ^{[1
]}

Srivastava, A

Gallivan, K

机构：

[1] Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA

[2] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA

[3] Florida State Univ, Sch Comp Sci & Informat Technol, Tallahassee, FL 32306 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2004年 / 26卷 / 05期

基金：

美国国家科学基金会;

关键词：

optimal subspaces; Grassmann manifold; object recognition; linear representations; dimension reduction; optimal component analysis;

D O I：

10.1109/TPAMI.2004.1273986

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.

引用

页码：662 / 666

页数：5

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