Improving Ancient Roman Coin Classification by Fusing Exemplar-Based Classification and Legend Recognition

  • Sebastian Zambanini
  • Albert Kavelar
  • Martin Kampel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)


In this paper we present an image-based classification method for ancient Roman Republican coins that uses multiple sources of information. Exemplar-based classification, which estimates the coins’ visual similarity by means of a dense correspondence field, and lexicon-based legend recognition are unified to a common classification approach. Classification scores from both coin sides are further integrated to an overall score determining the final classification decision. Experiments carried out on a dataset of 60 different classes comprising 464 coin images show that the combination of methods leads to higher classification rate than using them separately.


Support Vector Machine Image Match Visual Similarity Sift Feature Sift Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastian Zambanini
    • 1
  • Albert Kavelar
    • 1
  • Martin Kampel
    • 1
  1. 1.Computer Vision LabVienna University of TechnologyAustria

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