Advertisement

Towards Automatic Detection of CBIRs Configuration

  • Christian Vilsmaier
  • Rolf Karp
  • Mario Döller
  • Harald Kosch
  • Lionel Brunie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7131)

Abstract

Many Content Based Image Retrieval systems (CBIRs) have been invented in the last decade. The general mechanism of the search process is very similar for each of these CBIRs, and the calculation of rankings is determined by the comparison of features (low-, mid-, high-level). Nevertheless, all things being equal, the respective realization leads to different results. Knowledge about the internal configuration (used features, weights and metrics) of these systems would be beneficial in many usage scenarios (e.g., by using a query image content sensitive query forwarding strategy or improved result ranking strategies in meta search engines). In this context, the paper presents an approach that supports an automatic detection of the configuration of CBIR systems. We demonstrate that the problem can be partly traced back to an optimization problem and tested several optimization algorithms. The approach has been evaluated based on the ImageCLEF test set and shows good results.

Keywords

CBIRs configuration Image Database Low-level Feature detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barton, S., Dohnal, V., Sedmidubsky, J., Zezula, P.: Building self-organized image retrieval network. In: Proceeding of the 2008 ACM Workshop on Large-Scale Distributed Systems for Information Retrieval, pp. 51–58 (2008)Google Scholar
  2. 2.
    Datta, R., Joshi, D., Li, J., Wang, J.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) (January 2008)Google Scholar
  3. 3.
    Eidenberger, H.: Evaluation of content-based image descriptors by statistical methods. Multimedia Tools and Applications 35(3), 241–258 (2007)CrossRefGoogle Scholar
  4. 4.
    Black, J.A., Fahmy, G., Panchanathan, S.: A Method for Evaluating the Performance of Content-Based Image Retrieval Systems Based on Subjectively Determined Similarity between Images. In: Lew, M., Sebe, N., Eakins, J.P. (eds.) CIVR 2002. LNCS, vol. 2383, pp. 356–366. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    John Ashworth Nelder, R.M.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.C.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  7. 7.
    Kosch, H., Maier, P.: Content-based image retrieval systems - reviewing and benchmarking. 54 Journal of Digital Information Management (8), 1–21 (March 2010)Google Scholar
  8. 8.
    Lux, M., Chatzichristofis, S.: Lire: lucene image retrieval: an extensible java cbir library. In: Proceeding of the 16th ACM International Conference on Multimedia, pp. 1085–1088. LIRE (2008)Google Scholar
  9. 9.
    Müller, H., Müller, W., Marchand-Maillet, S., Pun, T., Squire, D.M.: Learning feature weights from user behavior in content-based image retrieval. In: Proceedings of the International Workshop on Multimedia Data Mining, Boston, USA, pp. 67–72. ACM (2000)Google Scholar
  10. 10.
    Müller, H., Tsikrika, T.: Global pattern recognition: The imageclef benchmark. IAPR Newsletter 32(1), 3–6 (2010)Google Scholar
  11. 11.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  12. 12.
    Stegmaier, F., Döller, M., Kosch, H., Hutter, A., Riegel, T.: AIR: Architecture for Interoperable Retrieval on distributed and heterogeneous Multimedia Repositories. In: Proceedings of the 11th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2010), Desenzano del Garda, Italy, pp. 1–4. IEEExplore (2010)Google Scholar
  13. 13.
    Thomas Deselaers, D.K., Ney, H.: Features for image retrieval: an experimental comparison. Information Retrieval 11(2), 77–107 (2007)CrossRefGoogle Scholar
  14. 14.
    Veltkamp, R., Tanase, M.: A survey of content-based image retrieval systems. In: Content-based Image and Video Retrieval, pp. 47–101 (2002)Google Scholar
  15. 15.
    Kore, S., Kondekar, V.H., Kolkure, V.S.: Image retrieval techniques based on image features: A state of art approach forcb ir. International Journal of Computer Science and Information Security 7(1), 69–76 (2010)Google Scholar
  16. 16.
    Yang, X., Deb, S.: Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 1(4), 330–343 (2010)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Vilsmaier
    • 2
  • Rolf Karp
    • 1
  • Mario Döller
    • 1
  • Harald Kosch
    • 1
  • Lionel Brunie
    • 2
  1. 1.Distributed and Multimedia Information SytemsUniversity of PassauPassauGermany
  2. 2.INSA de Lyon, LIRISVilleurbanne CedexFrance

Personalised recommendations