Classification and retrieval of ancient watermarks

Gerd Brunner, Hans Burkhardt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Watermarks in papers have been in use since 1282 in Medieval Europe. Watermarks can be understood much in the sense of being an ancient form of a copyright signature. The interest of the International Association of Paper Historians (IPH) lies specifically in the categorical determination of similar ancient watermark signatures. The highly complex structure of watermarks can be regarded as a strong and discriminative property. Therefore we introduce edge-based features that are incorporated for retrieval and classification. The feature extraction method is capable of representing the global structure of the watermarks, as well as local perceptual groups and their connectivity. The advantage of the method is its invariance against changes in illumination and similarity transformations. The classification results have been obtained with leave-one out tests and a support vector machine (SVM) with an intersection kernel. The best retrieval results have been received with the histogram intersection similarity measure. For the 14 class problem we obtain a true positive rate of more than 87%, that is better than any earlier attempt.

Original languageEnglish (US)
Title of host publicationData Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007
PublisherKluwer Academic Publishers
Pages237-244
Number of pages8
ISBN (Print)9783540782391
DOIs
StatePublished - 2008
Event31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007 - Freiburg, Germany
Duration: Mar 7 2007Mar 9 2007

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814

Conference

Conference31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007
CountryGermany
CityFreiburg
Period3/7/073/9/07

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Analysis

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