Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorCooper, Jessica
dc.contributor.authorArandjelovic, Ognjen
dc.contributor.editorOneto, Luca
dc.contributor.editorNavarin, Nicolò
dc.contributor.editorSperduti, Alessandro
dc.contributor.editorAnguita, Davide
dc.date.accessioned2020-02-18T15:30:07Z
dc.date.available2020-02-18T15:30:07Z
dc.date.issued2020
dc.identifier.citationCooper , J & Arandjelovic , O 2020 , Understanding ancient coin images . in L Oneto , N Navarin , A Sperduti & D Anguita (eds) , Recent Advances in Big Data and Deep Learning . Proceedings of the International Neural Networks Society , vol. 1 , Springer , Cham , pp. 330-340 , INNS Big Data and Deep Learning , Genova , Italy , 16/04/19 . https://doi.org/10.1007/978-3-030-16841-4_34en
dc.identifier.citationconferenceen
dc.identifier.isbn9783030168407
dc.identifier.isbn9783030168414
dc.identifier.issn2661-8141
dc.identifier.otherPURE: 266435084
dc.identifier.otherPURE UUID: 25128a89-c5cd-4c9e-ab7d-5ffe5e7caa79
dc.identifier.otherArXiv: http://arxiv.org/abs/1903.02665v2
dc.identifier.urihttp://hdl.handle.net/10023/19490
dc.description.abstractIn recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in the published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. Firstly, we explain that the approach of visual matching of coins, universally adopted in all existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g. online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on the understanding of the semantic content of coins. Hence, we describe a novel method which uses real-world multimodal input to extract and associate semantic concepts with the correct coin images and then using a novel convolutional neural network learn the appearance of these concepts. Empirical evidence on a real-world and by far the largest data set of ancient coins, we demonstrate highly promising results.
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofRecent Advances in Big Data and Deep Learningen
dc.relation.ispartofseriesProceedings of the International Neural Networks Societyen
dc.rightsCopyright © 2020 Springer Nature Switzerland AG. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-16841-4_34en
dc.subjectCJ Numismaticsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject3rd-NDASen
dc.subject.lccCJen
dc.subject.lccQA75en
dc.titleUnderstanding ancient coin imagesen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1007/978-3-030-16841-4_34
dc.identifier.urlhttp://arxiv.org/abs/1903.02665en


This item appears in the following Collection(s)

Show simple item record