Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFLFUL |
Repository | sid.inpe.br/sibgrapi/2017/08.20.19.06 |
Last Update | 2017:08.20.19.06.51 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.20.19.06.51 |
Metadata Last Update | 2020:02.19.02.01.27 administrator |
Citation Key | BombonatoCamaSilv:2017:ReSiBr |
Title | Real-time single-shot brand logo recognition  |
Format | On-line |
Year | 2017 |
Date | Oct. 17-20, 2017 |
Access Date | 2021, Jan. 21 |
Number of Files | 1 |
Size | 6945 KiB |
Context area | |
Author | 1 Bombonato, Leonardo 2 Camara-Chavez, Guillermo 3 Silva, Pedro |
Affiliation | 1 Universidade Federal de Ouro Preto 2 Universidade Federal de Ouro Preto 3 Universidade Federal de Ouro Preto |
Editor | Torchelsen, 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 |
e-Mail Address | leonardobombonato@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-20 19:06:51 :: leonardobombonato@gmail.com -> administrator :: 2020-02-19 02:01:27 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | computer vision, logo recognition, deep learning. |
Abstract | The 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PFLFUL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFLFUL |
Language | pt |
Target File | Sibgrapi Final Version.pdf |
User Group | leonardobombonato@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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