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Sparse representations of image gradient orientations for visual recognition and tracking

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

Authors

Georgios Tzimiropoulos

Stefanos Zafeiriou

Maja Pantic



Abstract

Recent results [18] have shown that sparse linear representations of a query object with respect to an overcomplete basis formed by the entire gallery of objects of interest can result in powerful image-based object recognition schemes. In this paper, we propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations. We show that minimal `1 solutions to problems formulated with gradient orientations can be used for fast and robust object recognition even for probe objects corrupted by outliers. These solutions are obtained without the need for solving the extended problem considered in [18]. We further show that low-dimensional embeddings generated from gradient orientations perform equally well even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings. We demonstrate experimentally that, compared to the baseline method in [18], our formulation results in better recognition rates without the need for block processing and even with smaller number of training samples. Finally, based on our results, we also propose a robust and efficient `1-based “tracking by detection” algorithm. We show experimentally that our tracker outperforms a recently proposed `1-based tracking algorithm in terms of robustness, accuracy and speed.

Publication Date Jan 1, 2011
Peer Reviewed Peer Reviewed
APA6 Citation Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2011). Sparse representations of image gradient orientations for visual recognition and tracking
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5981809
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2011 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.
Published in: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) IEEE, 2011, ISBN 9781457705298.
doi: 10.1109/CVPRW.2011.5981809

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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