Active Learning for Classifying Template Matches in Historical Maps

  • Benedikt BudigEmail author
  • Thomas C. van Dijk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9356)


Historical maps are important sources of information for scholars of various disciplines. Many libraries are digitising their map collections as bitmap images, but for these collections to be most useful, there is a need for searchable metadata. Due to the heterogeneity of the images, metadata are mostly extracted by hand—if at all: many collections are so large that anything more than the most rudimentary metadata would require an infeasible amount of manual effort. We propose an active-learning approach to one of the practical problems in automatic metadata extraction from historical maps: locating occurrences of image elements such as text or place markers. For that, we combine template matching (to locate possible occurrences) with active learning (to efficiently determine a classification). Using this approach, we design a human computer interaction in which large numbers of elements on a map can be located reliably using little user effort. We experimentally demonstrate the effectiveness of this approach on real-world data.


Active learning Threshold detection Human computer interaction Template matching Historical maps Knowledge discovery 



We thank Wouter Duivesteijn for fruitful discussion and helpful comments. We thank Hans-Günter Schmidt of the Würzburg University Library for providing real data and practical use cases.


  1. 1.
    Arteaga, M.G.: Historical map polygon and feature extractor. In: Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction, pp. 66–71 (2013)Google Scholar
  2. 2.
    Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Bryan, B., Nichol, R.C., Genovese, C.R., Schneider, J., Miller, C.J., Wasserman, L.: Active learning for identifying function threshold boundaries. Adv. Neural Inf. Process. Syst. 18, 163–170 (2006)Google Scholar
  4. 4.
    Chen, Y., Krause, A.: Near-optimal batch mode active learning and adaptive submodular optimization. In: Proceedings of the 30th International Conference on Machine Learning, pp. 160–168 (2013)Google Scholar
  5. 5.
    Deseilligny, M.P., Le Men, H., Stamon, G.: Character string recognition on maps, a rotation-invariant recognition method. Pattern Recogn. Lett. 16(12), 1297–1310 (1995)CrossRefGoogle Scholar
  6. 6.
    Donmez, P., Carbonell, J.G.: Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 619–628 (2008)Google Scholar
  7. 7.
    Fleet, C., Kowal, K.C., Pridal, P.: Georeferencer: crowdsourced georeferencing for map library collections. D-Lib Mag. 18(11/12) (2012)Google Scholar
  8. 8.
    Guo, Y., Schuurmans, D.: Discriminative batch mode active learning. In: Advances in Neural Information Processing Systems 20, Proceedings of the 21st Annual Conference on Neural Information Processing Systems, pp. 593–600 (2007)Google Scholar
  9. 9.
    Höhn, W.: Detecting arbitrarily oriented text labels in early maps. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 424–432. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    Höhn, W., Schmidt, H.G., Schöneberg, H.: Semiautomatic recognition and georeferencing of places in early maps. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 335–338 (2013)Google Scholar
  11. 11.
    Hoi, S., Jin, R., Zhu, J., Lyu, M.: Batch mode active learning and its application to medical image classification. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 417–424 (2006)Google Scholar
  12. 12.
    Holzinger, A.: Human-computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Jenny, B., Hurni, L.: Cultural heritage: studying cartographic heritage: analysis and visualization of geometric distortions. Comput. Graph. 35(2), 402–411 (2011)CrossRefGoogle Scholar
  14. 14.
    Leyk, S., Boesch, R., Weibel, R.: Saliency and semantic processing: extracting forest cover from historical topographic maps. Pattern Recogn. 39(5), 953–968 (2006)CrossRefGoogle Scholar
  15. 15.
    Mello, C.A.B., Costa, D.C., dos Santos, T.J.: Automatic image segmentation of old topographic maps and floor plans. In: Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics, pp. 132–137 (2012)Google Scholar
  16. 16.
    Parker, C.: An analysis of performance measures for binary classifiers. In: Proceedings of the 11th International Conference on Data Mining, pp. 517–526 (2011)Google Scholar
  17. 17.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Schein, A.I., Ungar, L.H.: Active learning for logistic regression: an evaluation. Mach. Learn. 68(3), 235–265 (2007)CrossRefGoogle Scholar
  19. 19.
    Schöneberg, H., Schmidt, H.G., Höhn, W.: A scalable, distributed and dynamic workflow system for digitization processes. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 359–362 (2013)Google Scholar
  20. 20.
    Settles, B.: Active learning literature survey. Computer Sciences Technical report 1648, University of Wisconsin-Madison (2010)Google Scholar
  21. 21.
    Settles, B.: Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers, San Rafael (2012)zbMATHGoogle Scholar
  22. 22.
    Shaw, T., Bajcsy, P.: Automation of digital historical map analyses. In: Proceedings of the IS&T/SPIE Electronic Imaging 2011, vol. 7869 (2011)Google Scholar
  23. 23.
    Simon, R., Haslhofer, B., Robitza, W., Momeni, E.: Semantically augmented annotations in digitized map collections. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 199–202 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Chair of Computer Science IUniversität WürzburgWürzburgGermany

Personalised recommendations