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.
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
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)
Datta, R., Joshi, D., Li, J., Wang, J.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) (January 2008)
Eidenberger, H.: Evaluation of content-based image descriptors by statistical methods. Multimedia Tools and Applications 35(3), 241–258 (2007)
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)
John Ashworth Nelder, R.M.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
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)
Kosch, H., Maier, P.: Content-based image retrieval systems - reviewing and benchmarking. 54 Journal of Digital Information Management (8), 1–21 (March 2010)
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)
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)
Müller, H., Tsikrika, T.: Global pattern recognition: The imageclef benchmark. IAPR Newsletter 32(1), 3–6 (2010)
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)
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)
Thomas Deselaers, D.K., Ney, H.: Features for image retrieval: an experimental comparison. Information Retrieval 11(2), 77–107 (2007)
Veltkamp, R., Tanase, M.: A survey of content-based image retrieval systems. In: Content-based Image and Video Retrieval, pp. 47–101 (2002)
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)
Yang, X., Deb, S.: Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 1(4), 330–343 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vilsmaier, C., Karp, R., Döller, M., Kosch, H., Brunie, L. (2012). Towards Automatic Detection of CBIRs Configuration. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-27355-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27354-4
Online ISBN: 978-3-642-27355-1
eBook Packages: Computer ScienceComputer Science (R0)