Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees

  • Raphaël Marée
  • Pierre Geurts
  • Louis Wehenkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4844)


We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images with state-of-the-art results despite its conceptual simplicity and computational efficiency.


Recognition Rate Image Retrieval Training Image Query Image Scalable Invariant Feature Transform 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces - index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3), 322–373 (2001)CrossRefGoogle Scholar
  2. 2.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 39(65) (2007)Google Scholar
  3. 3.
    Deselaers, T., Keysers, D., Ney, H.: Classification error rate for quantitative evaluation of content-based image retrieval systems. In: ICPR 2004. Proc. 17th International Conference on Pattern Recognition, pp. 505–508 (2004)Google Scholar
  4. 4.
    Deselaers, T., Keysers, D., Ney, H.: Discriminative training for object recognition using image patches. In: CVPR 2005. Proc. International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 157–162 (2005)Google Scholar
  5. 5.
    Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3), 209–226 (1977)zbMATHCrossRefGoogle Scholar
  6. 6.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 36(1), 3–42 (2006)CrossRefGoogle Scholar
  7. 7.
    Geurts, P., Wehenkel, L., d’Alché Buc, F.: Kernelizing the output of tree-based methods. In: ICML 2006. Proc. of the 23rd International Conference on Machine Learning, pp. 345–352. ACM, New York (2006)CrossRefGoogle Scholar
  8. 8.
    Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: Proc. IEEE CVPR, vol. 1, pp. 34–40. IEEE, Los Alamitos (2005)Google Scholar
  9. 9.
    Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: Proc. IEEE CVPR, vol. 2, pp. 2161–2168 (2006)Google Scholar
  10. 10.
    Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: Proc. IEEE CVPR, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  11. 11.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 490–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Obdržálek, S., Matas, J.: Image retrieval using local compact DCT-based representation. In: Michaelis, B., Krell, G. (eds.) Pattern Recognition. LNCS, vol. 2781, pp. 490–497. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Obdržálek, S., Matas, J.: Sub-linear indexing for large scale object recognition. In: BMVC 2005. Proc. British Machine Vision Conference, pp. 1–10 (2005)Google Scholar
  14. 14.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proc. IEEE CVPR, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  15. 15.
    Schmid, C., Mohr, R.: Local greyvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–534 (1997)CrossRefGoogle Scholar
  16. 16.
    Shao, H., Svoboda, T., Ferrari, V., Tuytelaars, T., Van Gool, L.: Fast indexing for image retrieval based on local appearance with re-ranking. In: ICIP 2003. Proc. IEEE International Conference on Image Processing, pp. 737–749. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  17. 17.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  18. 18.
    Zhang, J., Marszaek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision 73, 213–238 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Raphaël Marée
    • 1
  • Pierre Geurts
    • 2
  • Louis Wehenkel
    • 2
  1. 1.GIGA Bioinformatics Platform, University of LiègeBelgium
  2. 2.Systems and Modeling Unit, Montefiore Institute, University of LiègeBelgium

Personalised recommendations