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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Kim, C.: Content-based image copy detection. Signal Processing: Image Communication 18(3), 169–184 (2003)
Craver, S.: Zero Knowledge Watermark Detection. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 101–116. Springer, Heidelberg (2000)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
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)
Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: The 11th European Conference on Artificial Intelligence (1994)
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)
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).
Laaksonen, J., Koskela, M., Oja, E.: PicSOM - Self-Organizing Image Retrieval With MPEG-7 Content Descriptors. IEEE Transactions on Neural Networks (2002)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM - content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21 (2000)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: The PicSOM Retrieval System: Description and Evaluations. In: Proceedings of CIR-2000, Brighton, UK (2000)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prentis, P., Sjöberg, M., Koskela, M., Laaksonen, J. (2009). Image Theft Detection with Self-Organising Maps. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)