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SIR: The Smart Image Retrieval Engine

  • Jakub Lokoč
  • Tomáš Grošup
  • Tomáš Skopal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7404)

Abstract

We present the Smart Image Retrieval meta-search engine that allows content-based exploration of the results obtained from various sources (mostly based on keyword query). The online feature extraction architecture and exploration models utilizing single-/multi-query approaches are the two key features of our demo application that shows very promising results.

Keywords

Keyword Query Quadratic Form Distance Incremental Model Learning Demo Application Image Feature Signature 
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.

References

  1. 1.
    Beecks, C., Uysal, M.S., Seidl, T.: Signature quadratic form distance. In: Proc. ACM CIVR, pp. 438–445 (2010)Google Scholar
  2. 2.
    Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from google”s image search. In: IEEE Int. Conf. on Computer Vision, ICCV 2005, pp. 1816–1823. IEEE Computer Society, Washington, DC (2005)Google Scholar
  3. 3.
    Grošup, T., Lokoč, J., Skopal, T.: Smart Image Retrieval (SIR), SIRET Research Group (2012), http://www.siret.cz/sir
  4. 4.
    Li, L.-J., Fei-Fei, L.: Optimol: Automatic online picture collection via incremental model learning. Int. J. Comput. Vision 88(2), 147–168 (2010)CrossRefGoogle Scholar
  5. 5.
    Manjunath, B.S., Salembier, P., Sikora, T. (eds.): Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, Inc. (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jakub Lokoč
    • 1
  • Tomáš Grošup
    • 1
  • Tomáš Skopal
    • 1
  1. 1.SIRET Research Group, Dept. of Software Engineering, Faculty of Mathematics and PhysicsCharles University in PragueCzech Republic

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