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Authors: Andras Tüzkö 1 ; Christian Herrmann 2 ; Daniel Manger 1 and Jürgen Beyerer 2

Affiliations: 1 Fraunhofer IOSB, Germany ; 2 Fraunhofer IOSB and Karlsruhe Institute of Technology KIT, Germany

ISBN: 978-989-758-290-5

Keyword(s): Logo Detection, Logo Retrieval, Logo Dataset, Trademark Retrieval, Open Set Retrieval, Deep Learning.

Abstract: Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, signif icant performance improvements are observed, especially compared with state-of-the-art closed set approaches. (More)

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Paper citation in several formats:
Tüzkö, A.; Herrmann, C.; Manger, D. and Beyerer, J. (2018). Open Set Logo Detection and Retrieval.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-290-5, pages 284-292. DOI: 10.5220/0006614602840292

@conference{visapp18,
author={Andras Tüzkö. and Christian Herrmann. and Daniel Manger. and Jürgen Beyerer.},
title={Open Set Logo Detection and Retrieval},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2018},
pages={284-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006614602840292},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - Open Set Logo Detection and Retrieval
SN - 978-989-758-290-5
AU - Tüzkö, A.
AU - Herrmann, C.
AU - Manger, D.
AU - Beyerer, J.
PY - 2018
SP - 284
EP - 292
DO - 10.5220/0006614602840292

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