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

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

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.

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), https://doi.org/10.1109/TNNLS.2012.2208654

• Journal Article Type: Article
• Publication Date: Sep 10, 2012
• Deposit Date: Jan 29, 2016
• Publicly Available Date: Jan 29, 2016
• 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
• DOI: https://doi.org/10.1109/TNNLS.2012.2208654
• Keywords: Face Recognition, Gradient Methods, Learning (Artificial Intelligence), Object Tracking, Principal Component Analysis
• Public URL: https://nottingham-repository.worktribe.com/output/711489
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269106
• 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.