Chen, R., Hawes, M., Mihaylova, L. et al. (2 more authors) (2016) Vehicle Logo Recognition by Spatial-SIFT Combined with Logistic Regression. In: Information Fusion (FUSION), 2016 19th International Conference on. FUSION 2016, July 5th to July 8th 2016, Heidelberg, Germany. IEEE ISBN 978-0-9964-5274-8
Abstract
An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99.93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. This is an author produced version of a paper subsequently published in Information Fusion (FUSION), 2016 19th International Conference on. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2016 08:32 |
Last Modified: | 29 Nov 2016 13:41 |
Published Version: | http://ieeexplore.ieee.org/document/7528025/ |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99743 |