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Automated curation of brand-related social media images with deep learning
dc.contributor.author | Tous Liesa, Rubén |
dc.contributor.author | Gómez Parada, Mauro |
dc.contributor.author | Poveda, Jonatan |
dc.contributor.author | Cruz, Leonel |
dc.contributor.author | Wust, Otto |
dc.contributor.author | Makni, Mouna |
dc.contributor.author | Ayguadé Parra, Eduard |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2018-09-20T07:27:11Z |
dc.date.available | 2019-04-04T00:30:46Z |
dc.date.issued | 2018-10 |
dc.identifier.citation | Tous, R., Gómez, M., Poveda, J., Cruz, L., Wust, O., Makni, M., Ayguadé, E. Automated curation of brand-related social media images with deep learning. "Multimedia tools and applications", Octubre 2018, vol. 77, núm. 20, p. 27123-27142. |
dc.identifier.issn | 1380-7501 |
dc.identifier.uri | http://hdl.handle.net/2117/121321 |
dc.description.abstract | This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation. |
dc.format.extent | 20 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | User-generated content |
dc.subject.lcsh | Social media |
dc.subject.lcsh | Information retrieval |
dc.subject.other | |
dc.subject.other | |
dc.subject.other | Deep learning |
dc.subject.other | Marketing |
dc.title | Automated curation of brand-related social media images with deep learning |
dc.type | Article |
dc.subject.lemac | Mitjans de comunicació social |
dc.subject.lemac | Recuperació de la informació |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1007/s11042-018-5910-z |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/article/10.1007%2Fs11042-018-5910-z |
dc.rights.access | Open Access |
local.identifier.drac | 23193210 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ |
local.citation.author | Tous, R.; Gómez, M.; Poveda, J.; Cruz, L.; Wust, O.; Makni, M.; Ayguadé, E. |
local.citation.publicationName | Multimedia tools and applications |
local.citation.volume | 77 |
local.citation.number | 20 |
local.citation.startingPage | 27123 |
local.citation.endingPage | 27142 |
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