RERANKING WITH CONTEXTUAL DISSIMILARITY MEASURES FROM REPRESENTATIONAL BREGMAN K-MEANS

Olivier Schwander, Frank Nielsen

Abstract

We present a novel reranking framework for Content Based Image Retrieval (CBIR) systems based on contextual dissimilarity measures. Our work revisit and extend the method of Perronnin et al. (Perronnin et al., 2009) which introduces a way to build contexts used in turn to design contextual dissimilarity measures for reranking. Instead of using truncated rank lists from a CBIR engine as contexts, we rather use a clustering algorithm to group similar images from the rank list. We introduce the representational Bregman divergences and further generalize the Bregman k-means clustering by considering an embedding representation. These representation functions allows one to interpret a-divergences/projections as Bregman divergences/projections on a-representations. Finally, we validate our approach by presenting some experimental results on ranking performances on the INRIA Holidays database.

References

  1. Amari, S. (2007). Integration of stochastic models by minimizing a-divergence. Neural computation, 19(10):2780-2796.
  2. Amari, S. and Nagaoka, H. (2007). Methods of information geometry. AMS.
  3. Banerjee, A., Merugu, S., Dhillon, I., and Ghosh, J. (2005). Clustering with Bregman divergences. The Journal of Machine Learning Research, 6:1705-1749.
  4. Chentsov, N. (1982). Statistical Decision Rules and Optimal Inferences. Trans. of Math. Monog., n 53.
  5. Csiszár, I. (2008). Axiomatic characterizations of information measures. Entropy, 10(3):261-273.
  6. Datta, R., Joshi, D., Li, J., and Wang, J. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv.
  7. Douze, M., Jégou, H., Singh, H., Amsaleg, L., and Schmid, C. (2009). Evaluation of GIST descriptors for webscale image search. In International Conference on Image and Video Retrieval. ACM.
  8. Jégou, H., Douze, M., and Schmid, C. (2008). Hamming embedding and weak geometric consistency for large scale image search. In European conference on computer vision, pages 304-317. Springer.
  9. Jégou, H., Harzallah, H., and Schmid, C. (2007). A contextual dissimilarity measure for accurate and efficient image search. In Conference on Computer Vision & Pattern Recognition.
  10. Mihoko, M. and Eguchi, S. (2002). Robust blind source separation by beta divergence. Neural computation, 14(8):1859-1886.
  11. Nielsen, F. and Nock, R. (2009). The dual Voronoi diagrams with respect to representational Bregman divergences. In International Symposium on Voronoi Diagrams (ISVD).
  12. Nock, R., Luosto, P., and Kivinen, J. (2008). Mixed bregman clustering with approximation guarantees. In Daelemans, W., Goethals, B., and Morik, K., editors, ECML PKDD (2), volume 5212 of Lecture Notes in Computer Science, pages 154-169. Springer.
  13. Oliva, A. and Torralba, A. (2006). Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research, 155:23.
  14. Perronnin, F., Liu, Y., and Renders, J. (2009). A family of contextual measures of similarity between distributions application to image retrieval. In CVPR09, pages 2358-2365.
  15. Sivic, J. and Zisserman, A. (2003). Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV, volume 2, pages 1470-1477. Citeseer.
  16. Wu, S. and Amari, S. (2002). Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers. Neural Processing Letters, 15(1):59-67.
Download


Paper Citation


in Harvard Style

Schwander O. and Nielsen F. (2010). RERANKING WITH CONTEXTUAL DISSIMILARITY MEASURES FROM REPRESENTATIONAL BREGMAN K-MEANS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 118-123. DOI: 10.5220/0002842901180123


in Bibtex Style

@conference{visapp10,
author={Olivier Schwander and Frank Nielsen},
title={RERANKING WITH CONTEXTUAL DISSIMILARITY MEASURES FROM REPRESENTATIONAL BREGMAN K-MEANS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={118-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002842901180123},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - RERANKING WITH CONTEXTUAL DISSIMILARITY MEASURES FROM REPRESENTATIONAL BREGMAN K-MEANS
SN - 978-989-674-028-3
AU - Schwander O.
AU - Nielsen F.
PY - 2010
SP - 118
EP - 123
DO - 10.5220/0002842901180123