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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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