Content-Based Coin Retrieval Using Invariant Features and Self-organizing Maps

  • Nikolaos Vassilas
  • Christos Skourlas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


During the last years, Content-Based Image Retrieval (CBIR) has developed to an important research domain within the context of multimodal information retrieval. In the coin retrieval application dealt in this paper, the goal is to retrieve images of coins that are similar to a query coin based on features extracted from color or grayscale images. To assure improved performance at various scales, orientations or in the presence of noise, a set of global and local invariant features is proposed. Experimental results using a Euro coin database show that color moments as well as edge gradient shape features, computed at five concentric equal-area rings, compare favorably to wavelet features. Moreover, combinations of the above features using L1 or L2 similarity measures lead to excellent retrieval capabilities. Finally, color quantization of the database images using self-organizing maps not only leads to memory savings but also it is shown to even improve retrieval accuracy.


Query Image Retrieval Accuracy CBIR System Wavelet Feature Local Invariant Feature 


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  1. 1.
    Rui, Y., Huang, T.S., Chang, S.-F.: Image Retrieval: Current Techniques, Promising Directions and Open Issues. J. of Visual Communication and Image Representation 10, 1–23 (1999)CrossRefGoogle Scholar
  2. 2.
    Faloutsos, C., Oard, D.: A Survey of Information Retrieval and Filtering Methods. Tech. Rep. CS-TR-3514, Dept. of Computer Science, Univ. of Maryland, College Park (1995)Google Scholar
  3. 3.
    Veltkamp, R.C.: Content-Based Image Retrieval Systems: A Survey. Revision of Tech. Rep. UU-CS-2000-34, Dept. of Computer Science, Utrecht University (2002)Google Scholar
  4. 4.
    Eakins, J.P., Graham, M.E.: Content-Based Image Retrieval. Tech. Rep. JTAP-039, JISC Technology Application Program, Newcastle upon Tyne (2000)Google Scholar
  5. 5.
    Marinagi, C., Alevizos, T., Kaburlasos, V.G., Skourlas, C.: Fuzzy Interval Number (FIN) Techniques for Cross Language Information Retrieval. In: 8th ICEIS (May 2006) (accepted for publication)Google Scholar
  6. 6.
    Moreno, J.M., Madrenas, J., Cabestany, J., Launa, J.R.: Practical Design Methodology for Commercial Automatic Coin Recognizers based on Neural Decision Engines. In: Proc. Int. Conf. Neural Information Processing and Intelligent Information Systems, pp. 662–665 (1997)Google Scholar
  7. 7.
    Fukumi, M., Omatu, S., Takeda, F., Kosaka, T.: Rotation-Invariant Neural Pattern Recognition System with Application to Coin Recognition. IEEE Tr. on Neural Networks 3(2), 272–279 (1992)CrossRefGoogle Scholar
  8. 8.
    Zhang, M., Bhowan, U.: Program Size and Pixel Statistics in Genetic Programming for Object Detection. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 379–388. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Bremananth, R., Balaji, B., Sankar, M., Chitra, A.: A New Approach to Coin Recognition Using Neural Pattern Analysis. In: Proc. INDICON, Annual IEEE, pp. 366–370 (2005)Google Scholar
  10. 10.
    McNeill, S., Schipper, J., Sellers, T.: Coin Recognition Using Vector Quantization and Histogram Modeling. In: 17th Florida Conf. on Recent Advances in Robotics, FCRAR (2004),
  11. 11.
    Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of Coins Using an Eigenspace Approach. Pattern Recognition Letters, 61–75 (2005)Google Scholar
  12. 12.
    Ballard, D.H., Brown, C.M.: Computer Vision. Prentice Hall, Englewood Cliffs (1982)Google Scholar
  13. 13.
    Castleman, K.R.: Digital Image Processing. Prentice Hall, Upper Saddle River (1996)Google Scholar
  14. 14.
    Vassilas, N., Charou, E.: A New Methodology for Efficient Classification of Multispectral Satellite Images Using Neural Network Techniques. Neural Processing Letters 9(1), 35–43 (1998)CrossRefGoogle Scholar
  15. 15.
    Vassilas, N.: Efficient Neural Network-Based Methodology for the Design of Multiple Classifiers. In: Jain, L.C., Fanelli, A.-M. (eds.) Recent Advances in Artificial Neural Networks – Design and Applications, pp. 95–125. CRC Press, New York (2000)Google Scholar
  16. 16.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nikolaos Vassilas
    • 1
  • Christos Skourlas
    • 1
  1. 1.Department of InformaticsTechnological Educational Institute of Athens 

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