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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFLFUL
Repositorysid.inpe.br/sibgrapi/2017/08.20.19.06
Last Update2017:08.20.19.06.51 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.20.19.06.51
Metadata Last Update2022:06.14.00.08.51 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.24
Citation KeyBombonatoCamaSilv:2017:ReSiBr
TitleReal-time single-shot brand logo recognition
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size6945 KiB
2. Context
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
e-Mail Addressleonardobombonato@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-20 19:06:51 :: leonardobombonato@gmail.com -> administrator ::
2022-06-14 00:08:51 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Real-time single-shot brand...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Real-time single-shot brand...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFLFUL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFLFUL
Languagept
Target FileSibgrapi Final Version.pdf
User Groupleonardobombonato@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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