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Open set logo detection and retrieval

: Tüzkö, A.; Herrmann, C.; Manger, D.; Beyerer, Jürgen

Fulltext urn:nbn:de:0011-n-5100380 (3.0 MByte PDF)
MD5 Fingerprint: 60c2b20848dc1bc44aef4ae5f9d8d2dd
Created on: 14.5.2019

Imai, F. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
VISIGRAPP 2018, 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.5: VISAPP : Funchal, Madeira, Portugal, January 27-29, 2018
Setúbal: SciTePress, 2018
ISBN: 978-989-758-290-5
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <13, 2018, Funchal>
International Conference on Computer Vision Theory and Applications (VISAPP) <13, 2018, Funchal>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

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 Convolutional Neural Networks (CNNs). If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.