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Gaussian process classification as metric learning for forensic writer identification
University of Gävle, Faculty of Education and Business Studies, Department of Humanities, Swedish and Gender studies.
2018 (English)In: Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, 2018, p. 175-180Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed. An unsupervised feature learning approach, based on dense contour descriptor sampling, was combined with a novel way of learning a general space for clustering writer hands, in a forensic setting. The metric learning inference was based on multiclass Gaussian process classification. Using the popular datasets IAM and CVL combined, the evaluation was performed on close to 1000 writer hands. This paper builds on earlier work from our group on building a system for estimating the production dates of medieval manuscripts, and act as a foundation for future use of writer identification techniques on our historical data. © 2018 IEEE.

Place, publisher, year, edition, pages
2018. p. 175-180
Keywords [en]
component, digital paleography, document analysis, Gaussian process classification, unsupervised feature learning, writer identification, Forensic science, Gaussian distribution, Gaussian noise (electronic), Information retrieval systems, Learning systems, Gaussian process classifications, Classification (of information)
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hig:diva-27647DOI: 10.1109/DAS.2018.76Scopus ID: 2-s2.0-85050305663ISBN: 978-1-5386-3346-5 (electronic)OAI: oai:DiVA.org:hig-27647DiVA, id: diva2:1238940
Conference
2018 13th IAPR International Workshop on Document Analysis Systems (DAS), 24-27 April 2018, Vienna, Austria
Available from: 2018-08-15 Created: 2018-08-15 Last updated: 2018-08-15Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf