Abstract
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.
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.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 36(1), 3–42 (2006)
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)
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)
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: Proc. IEEE CVPR, vol. 2, pp. 2161–2168 (2006)
Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: Proc. IEEE CVPR, IEEE Computer Society Press, Los Alamitos (2007)
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)
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)
Obdržálek, S., Matas, J.: Sub-linear indexing for large scale object recognition. In: BMVC 2005. Proc. British Machine Vision Conference, pp. 1–10 (2005)
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)
Schmid, C., Mohr, R.: Local greyvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–534 (1997)
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)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marée, R., Geurts, P., Wehenkel, L. (2007). Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_60
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)