DC Field | Value | Language |
dc.contributor.author | Garita Figueiredo, R. | |
dc.contributor.author | Kühnberger, K.-U. | |
dc.contributor.author | Pipa, G. | |
dc.contributor.author | Thelen, T. | |
dc.contributor.editor | Tetko, I.V. | |
dc.contributor.editor | Karpov, P. | |
dc.contributor.editor | Theis, F. | |
dc.contributor.editor | Kurkova, V. | |
dc.date.accessioned | 2021-12-23T16:33:37Z | - |
dc.date.available | 2021-12-23T16:33:37Z | - |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9783030304898 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/17783 | - |
dc.description | Conference of 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference Date: 17 September 2019 Through 19 September 2019; Conference Code:231689 | |
dc.description.abstract | This document describes the implementation of a copyright classification process for user-contributed Portable Document Format (PDF) documents. The implementation employs two ways to classify documents as copyright-protected or non-copyright-protected: first, using structural features extracted from the document metadata, content and underlying document structure; and second, by turning the documents into images and using their pixels to generate features for semi-supervised deep convolutional networks. © 2019, Springer Nature Switzerland AG. | |
dc.language.iso | en | |
dc.publisher | Springer Verlag | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Copyrights | |
dc.subject | Information retrieval systems | |
dc.subject | Neural networks, Classification process | |
dc.subject | Convolutional networks | |
dc.subject | Document metadatas | |
dc.subject | Document structure | |
dc.subject | Feature-based classification | |
dc.subject | Portable document formats | |
dc.subject | Semi-supervised | |
dc.subject | Structural feature, Deep learning | |
dc.title | Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents | |
dc.type | conference paper | |
dc.identifier.doi | 10.1007/978-3-030-30490-4_7 | |
dc.identifier.scopus | 2-s2.0-85072869152 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072869152&doi=10.1007%2f978-3-030-30490-4_7&partnerID=40&md5=457e4d59c82d15cc22a4dc99e34c692f | |
dc.description.volume | 11730 LNCS | |
dc.description.startpage | 69 | |
dc.description.endpage | 75 | |
dcterms.isPartOf.abbreviation | Lect. Notes Comput. Sci. | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.dept | Zentrum VirtUOS | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.deptid | organisation31 | - |
crisitem.author.orcid | 0000-0003-1626-0598 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.orcid | 0000-0002-3337-6093 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.parentorg | Universität Osnabrück | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | KuKa032 | - |
crisitem.author.netid | PiGo340 | - |
crisitem.author.netid | ThTo467 | - |