Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition

被引：43

作者：

Liwicki, Stephan ^{[1
]}

Zafeiriou, Stefanos ^{[1
]}

Tzimiropoulos, Georgios ^{[1
,2
]}

Pantic, Maja ^{[1
,3
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2AZ, England

[2] Lincoln Univ, Sch Comp Sci, Lincoln LN6 7TS, England

[3] Univ Twente, Fac Elect Engn Math & Comp Sci, NL-7522 NB Enschede, Netherlands

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 10期

基金：

欧洲研究理事会;

关键词：

Gradient-based kernel; online kernel learning; principal component analysis with indefinite kernels; recognition; robust tracking; CLASSIFICATION; ROBUST; MODELS; SCALE;

D O I：

10.1109/TNNLS.2012.2208654

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an incremental KPCA in Krein space that does not require the calculation of preimages and therefore is both efficient and exact. Our approach has been motivated by the application of visual tracking for which we wish to employ a robust gradient-based kernel. We use the proposed nonlinear appearance model learned online via KPCA in Krein space for visual tracking in many popular and difficult tracking scenarios. We also show applications of our kernel framework for the problem of face recognition.

引用

页码：1624 / 1636

页数：13

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