Towards a Computer-Based Understanding of Medieval Images

  • Pradeep Yarlagadda
  • Antonio Monroy
  • Bernd Carqué
  • Björn Ommer
Conference paper
Part of the Contributions in Mathematical and Computational Sciences book series (CMCS, volume 3)

Abstract

Research in the field of cultural heritage requires computer vision algorithms that can automatically advance to the representational content of images. To make large scale image databases accessible it is crucial that computer-based object retrieval in images lives up to what it really is, a search through images rather than a search through textual annotations as in many current retrieval systems in cultural heritage. Our contribution is threefold: (1) Benchmarking: We have assembled a novel image dataset of medieval images with groundtruth information. Its completeness and scientific significance in the humanities allows, for the first time, to thoroughly evaluate retrieval algorithms for cultural heritage. (2) Object analysis: Object shape is automatically extracted from tinted drawings and we present a statistical algorithm that automatically analyzes the type-variability of object categories. The discovered category substructure is, in its richness, not captured by the currently used annotations. (3) Recognition: A category-level retrieval system is presented that detects objects in images and, thus, provides object locations which are not available in current metadata.

Keywords

Category level detection Content-based retrieval Object analysis 

References

  1. 1.
    Baca M, Harpring P, Lanzi E, McRae L, Whiteside A (2006) Cataloging cultural objects. A guide to describing cultural works and their images. American Library Association, ChicagoGoogle Scholar
  2. 2.
    Kerscher G (2008) Thesaurus-Verwendung und Internationalisierung in Bilddatenbanken. Kunstchronik 57:606–608Google Scholar
  3. 3.
    van Straten R (1994) Iconography, indexing, ICONCLASS. A handbook. Foleor, LeidenGoogle Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
    Fergus R, Perona P, Zisserman A (2003) Object class recognition by unsupervised scale-invariant learning. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), vol 2, pp 264–271Google Scholar
  10. 10.
    Ferrari V, Jurie F, Schmid C (2010) From images to shape models for object detection. Int J Comput Vis 87(3):284–303CrossRefGoogle Scholar
  11. 11.
    Ommer B, Malik J (2009) Multi-scale object detection by clustering lines. In: Proceedings of international conference on computer vision and pattern recognition (CVPR)Google Scholar
  12. 12.
    Leibe B, Leonardis A, Schiele B (2004) Combined object categorization and segmentation with an implicit shape model. In: European conference on computer vision workshop on statistical learning computer vision, Prague, pp 17–32Google Scholar
  13. 13.
    Lampert C, Blaschko M, Hofmann T (2008) Beyond sliding windows: object localization by efficient subwindow search. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), Max Planck Institute for Biological Cybernetics/Google Inc., Tubingen/ZurichGoogle Scholar
  14. 14.
    Pietzsch E, Effinger M, Spyra U (2003) Digitalisierung und Erschließung spätmittelalterlicher Bilderhandschriften aus der Bibliotheca Palatina. In: Thaller H (ed) Digitale Bausteine für die geisteswissenschaftliche Forschung, Fundus Beih, vol 5, pp 61–88Google Scholar
  15. 15.
    Schramm PE (1954–1956) Herrschaftszeichen und Staatssymbolik. Hiersemann, StuttgartGoogle Scholar
  16. 16.
    Schwedler G, Meyer C, Zimmermann K (eds) (2008) Rituale und die Ordnung der Welt. Winter, HeidelbergGoogle Scholar
  17. 17.
    Fowlkes C, Tal D, Martin D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), vol 2. Berkeley, pp 416–423Google Scholar
  18. 18.
    Roberts L (1965) Machine perception of three-dimensional solids. In: Tippett JT (ed) Optical and electrooptical information processing. Massachusetts Institute of Technology PR, Cambridge, MA, pp 159–197Google Scholar
  19. 19.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–714CrossRefGoogle Scholar
  20. 20.
    Arbelaez P, Fowlkes C, Maire M, Malik J (2008) Using contours to detect and localize junctions in natural images. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), AnchorageGoogle Scholar
  21. 21.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), vol 1, pp 886–893Google Scholar
  22. 22.
    Saurma-Jeltsch LE (2001) Spätformen mittelalterlicher Buchherstellung. Bilderhandschriften aus der Werkstatt Diebold Laubers in Hagenau. Reichert, WiesbadenGoogle Scholar
  23. 23.
    Maji S, Berg A, Malik J (2008) Classification using intersection kernel support vector machines. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), AnchorageGoogle Scholar
  24. 24.
    Petersohn J (1998) Über monarchische Insignien und ihre Funktion im mittelalterlichen Reich. Historische Zeitschrift 266:47–96Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pradeep Yarlagadda
    • 1
  • Antonio Monroy
    • 1
  • Bernd Carqué
    • 1
  • Björn Ommer
    • 1
  1. 1.Interdisciplinary Center for Scientific Computing & Transcultural StudiesUniversity of HeidelbergHeidelbergGermany

Personalised recommendations