dc.contributor.author | Riba, P | |
dc.contributor.author | Dutta, A | |
dc.contributor.author | Llados, J | |
dc.contributor.author | Fornes, A | |
dc.date.accessioned | 2019-10-15T12:03:16Z | |
dc.date.issued | 2018-01-29 | |
dc.description.abstract | Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and we show how they can be used in graphics recognition problems. | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | FPU | en_GB |
dc.description.sponsorship | Ministerio de Educación, Cultura y Deporte, Spain | en_GB |
dc.description.sponsorship | Ramon y Cajal Fellowship | en_GB |
dc.description.sponsorship | CERCA Program/Generalitat de Catalunya | en_GB |
dc.identifier.citation | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 9-15 November 2017, Kyoto, Japan, pp. 29-30 | en_GB |
dc.identifier.doi | 10.1109/ICDAR.2017.262 | |
dc.identifier.grantnumber | 665919 | en_GB |
dc.identifier.grantnumber | TIN2015-70924-C2-2-R | en_GB |
dc.identifier.grantnumber | FPU15/06264 | en_GB |
dc.identifier.grantnumber | RYC-2014-16831 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/39214 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2018 IEEE | en_GB |
dc.subject | Graphics | en_GB |
dc.subject | Message passing | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Data models | en_GB |
dc.subject | Computer vision | en_GB |
dc.subject | Convolutional neural networks | en_GB |
dc.title | Graph-Based Deep Learning for Graphics Classification | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2019-10-15T12:03:16Z | |
dc.identifier.isbn | 9781538635865 | |
dc.identifier.issn | 1520-5363 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-01-29 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2019-10-15T12:00:49Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-10-15T12:03:21Z | |
refterms.panel | B | en_GB |