@InProceedings{BombonatoCamaSilv:2017:ReSiBr,
author = "Bombonato, Leonardo and Camara-Chavez, Guillermo and Silva,
Pedro",
affiliation = "{Universidade Federal de Ouro Preto} and {Universidade Federal de
Ouro Preto} and {Universidade Federal de Ouro Preto}",
title = "Real-time single-shot brand logo recognition",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.24",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.24",
language = "pt",
ibi = "8JMKD3MGPAW/3PFLFUL",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFLFUL",
targetfile = "Sibgrapi Final Version.pdf",
urlaccessdate = "2024, May 02"
}