Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyBombonatoCamaSilv:2017:ReSiBr
TitleReal-time single-shot brand logo recognition
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size6945 KiB
Context area
Author1 Bombonato, Leonardo
2 Camara-Chavez, Guillermo
3 Silva, Pedro
Affiliation1 Universidade Federal de Ouro Preto
2 Universidade Federal de Ouro Preto
3 Universidade Federal de Ouro Preto
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-20 19:06:51 :: -> administrator ::
2020-02-19 02:01:27 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordscomputer vision, logo recognition, deep learning.
AbstractThe amount of data produced every day on theinternet increases every day and with the increasing popularityof the social networks the number of published photos arehuge, and those pictures contain several implicit or explicitbrand logos. Detecting this logos in natural images can provideinformation about how widespread is a brand, discover unwantedcopyright distribution, analyze marketing campaigns, etc. In thispaper, we propose a real-time brand logo recognition system thatoutperforms all other state-of-the-art in two different datasets.Our approach is based on the Single Shot MultiBox Detector(SSD), we explore this tool in a different domain and alsoexperiment the impact of training with pretrained weights andthe impact of warp transformations in the input images. Weconducted our experiments in two datasets, the FlickrLogos-32(FL32) and the Logos-32Plus (L32plus), which is an extension ofthe training set of the FL32. On the FL32, we outperform thestate-of-the-art by 2.5% the F-score and by 7.4% the recall. Forthe L32plus, we surpass the state-of-the-art by 1.2% the F-scoreand by 3.8% the recall.
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Target FileSibgrapi Final Version.pdf
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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