Optimal linear representations of images for object recognition

被引：68

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

共 13 条

[1] Natural gradient works efficiently in learning
Amari, S
[J]. NEURAL COMPUTATION, 1998, 10 (02) : 251 - 276
[2] Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection
Belhumeur, PN
Hespanha, JP
Kriegman, DJ
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (07) : 711 - 720
[3] The geometry of algorithms with orthogonality constraints
Edelman, A
Arias, TA
Smith, ST
[J]. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 1998, 20 (02) : 303 - 353
[4] A theory for learning by weight flow on Stiefel-Grassman manifold
Fiori, S
[J]. NEURAL COMPUTATION, 2001, 13 (07) : 1625 - 1647
[5] GALLIVAN K, 2003, P 12 IEEE WORKSH STA
[6] Fast and robust fixed-point algorithms for independent component analysis
Hyvärinen, A
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (03): : 626 - 634
[7] Optimization criteria and geometric algorithms for motion and structure estimation
Ma, Y
Kosecká, J
Sastry, S
[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2001, 44 (03) : 219 - 249
[8] PCA versus LDA
Martìnez, AM
Kak, AC
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2001, 23 (02) : 228 - 233
[9] Robert Christian P, 1999, Monte Carlo statistical methods, V2
[10] Sim T, 2003, IEEE T PATTERN ANAL, V25, P1615, DOI 10.1109/TPAMI.2003.1251154

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