Advertisement

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)

Abstract

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.

Keywords

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.

References

  1. 1.
    Arandjelovic, O.: Automatic attribution of ancient roman imperial coins. In: Conference on Computer Vision and Pattern Recognition, pp. 1728–1734 (2010)Google Scholar
  2. 2.
    Arandjelović, O.: Reading ancient coins: Automatically identifying denarii using obverse legend seeded retrieval. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 317–330. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bremananth, R., Balaji, B., Sankari, M., Chitra, A.: A new approach to coin recognition using neural pattern analysis. In: IEEE INDICON 2005, pp. 366–370 (2005)Google Scholar
  5. 5.
    Crawford, M.H.: Roman republican coinage. Cambridge University Press (1974)Google Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61, 55–79 (2005)CrossRefGoogle Scholar
  8. 8.
    Fukumi, M., Omatu, S., Takeda, F., Kosaka, T.: Rotation-invariant neural pattern recognition system with application to coin recognition. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1027–1032 (1991)Google Scholar
  9. 9.
    Grierson, P.: Numismatics. Oxford University Press (1975)Google Scholar
  10. 10.
    Huber-Mörk, R., Zambanini, S., Zaharieva, M., Kampel, M.: Identification of ancient coins based on fusion of shape and local features. Machine Vision Applications 22, 983–994 (2011)CrossRefGoogle Scholar
  11. 11.
    Kampel, M., Zaharieva, M.: Recognizing ancient coins based on local features. In: Bebis, G., et al. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 11–22. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Kavelar, A., Zambanini, S., Kampel, M.: Word detection applied to images of ancient roman coins. In: International Conference on Virtual Systems and Multimedia, pp. 577–580 (2012)Google Scholar
  13. 13.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Pattern Analysis and Machine Intelligence 33(5), 978–994 (2011)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    van der Maaten, L.J., Poon, P.: Coin-o-matic: A fast system for reliable coin classification. In: Muscle CIS Coin Competition Workshop, pp. 7–18 (2006)Google Scholar
  16. 16.
    Nölle, M., Penz, H., Rubik, M., Mayer, K., Holländer, I., Granec, R.: Dagobert - a new coin recognition and sorting system. In: 7th International Conference on Digital Image Computing - Techniques and Applications, pp. 329–338 (2003)Google Scholar
  17. 17.
    Reisert, M., Ronneberger, O., Burkhardt, H.: An efficient gradient based registration technique for coin recognition. In: Muscle CIS Coin Competition Workshop, pp. 19–31 (2006)Google Scholar
  18. 18.
    Shekhovtsov, A., Kovtun, I., Hlaváca, V.: Efficient mrf deformation model for non-rigid image matching. Computer Vision and Image Understanding 112(1), 91–99 (2008)CrossRefGoogle Scholar
  19. 19.
    Wang, K., Belongie, S.: Word spotting in the wild. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 591–604. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Zambanini, S., Kampel, M.: Coarse-to-fine correspondence search for classifying ancient coins. In: 2nd ACCV Workshop on e-Heritage, pp. 25–36 (2013)Google Scholar

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

Personalised recommendations