Image Theft Detection with Self-Organising Maps

  • Philip Prentis
  • Mats Sjöberg
  • Markus Koskela
  • Jorma Laaksonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

In this paper an application of the TS-SOM variant of the self-organising map algorithm on the problem of copyright theft detection for bitmap images is shown. The algorithm facilitates the location of originals of copied, damaged or modified images within a database of hundreds of thousands of stock images. The method is shown to outperform binary decision tree indexing with invariant frame detection.

Keywords

Image theft detection image retrieval self-organising maps TS-SOM PicSOM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Horáček, O., Bican, J., Kamenický, J., Flusser, J.: Image Retrieval for Image Theft Detection. In: The 5th International Conference on Computer Recognition Systems, Warsaw (2007)Google Scholar
  2. 2.
    Horáček, O., Bican, J., Kamenický, J., Flusser, J.: Image Retrieval for Image Theft Detection. In: Computer Recognition Systems 2. Advances in Soft Computing, vol. 45, pp. 44–51. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Kim, C.: Content-based image copy detection. Signal Processing: Image Communication 18(3), 169–184 (2003)Google Scholar
  4. 4.
    Craver, S.: Zero Knowledge Watermark Detection. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 101–116. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)CrossRefMATHGoogle Scholar
  6. 6.
    Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: The International Joint Conference on Neural Networks, San Diego, California, vol. II, pp. 279–284 (1990)Google Scholar
  7. 7.
    Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: The 11th European Conference on Artificial Intelligence (1994)Google Scholar
  8. 8.
    Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: Self-Organizing Maps as a Relevance Feedback Technique in Content Based Image Retrieval. Pattern analysis & Applications 4(2-3), 140–152 (2001)CrossRefMATHGoogle Scholar
  9. 9.
    Laaksonen, J., Koskela, M., Oja, E.: Application of Self-Organizing Maps in Content Based Image Retrieval. In: The 9th International Conference on Neural Networks, Edinburgh, (1999).Google Scholar
  10. 10.
    Laaksonen, J., Koskela, M., Oja, E.: PicSOM - Self-Organizing Image Retrieval With MPEG-7 Content Descriptors. IEEE Transactions on Neural Networks (2002)Google Scholar
  11. 11.
    Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM - content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21 (2000)Google Scholar
  12. 12.
    Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: The PicSOM Retrieval System: Description and Evaluations. In: Proceedings of CIR-2000, Brighton, UK (2000)Google Scholar
  13. 13.
    Laaksonen, J., Oja, J., Koskela, M., Brandt, S.: Analyzing Low-level Visual Features Using Content-Based Image Retrieval. In: The 7th International Conference on Neural Information Processing, Taejon, Korea (2000)Google Scholar
  14. 14.
    Viitaniemi, V., Laaksonen, J.: Keyword-detection approach to automatic image annotation. In: Proceedings of 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, London, UK (2005)Google Scholar
  15. 15.
    Prentis, P.: GalSOM - Colour-Based Image Browsing and Retrieval with Tree-Structured Self-Organising Maps. In: The 6th International Workshop on Self-Organizing Maps, Bielefeld, Germany (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Philip Prentis
    • 1
  • Mats Sjöberg
    • 1
  • Markus Koskela
    • 1
  • Jorma Laaksonen
    • 1
  1. 1.Czech Technical University in PragueHelsinki University of TechnologyFinland

Personalised recommendations