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Efficient online subspace learning with an indefinite kernel for visual tracking and recognition

Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

Authors

Stephan Liwicki

Stefanos Zafeiriou

Georgios Tzimiropoulos

Maja Pantic

Abstract

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.

Journal Article Type Article
Publication Date Sep 10, 2012
Journal Neural Networks and Learning Systems, IEEE Transactions on
Electronic ISSN 2162-237X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 10
Institution Citation Liwicki, S., Zafeiriou, S., Tzimiropoulos, G., & Pantic, M. (2012). Efficient online subspace learning with an indefinite kernel for visual tracking and recognition. IEEE Transactions on Neural Networks and Learning Systems, 23(10), doi:10.1109/TNNLS.2012.2208654
DOI https://doi.org/10.1109/TNNLS.2012.2208654
Keywords Face Recognition, Gradient Methods, Learning (Artificial Intelligence), Object Tracking, Principal Component Analysis
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269106
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information ©2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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