Towards Exhaustive Pairwise Matching in Large Image Collections

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)


Exhaustive pairwise matching on large datasets presents serious practical challenges, and has mostly remained an unexplored domain. We make a step in this direction by demonstrating the feasibility of scalable indexing and fast retrieval of appearance and geometric information in images. We identify unification of database filtering and geometric verification steps as a key step for doing this. We devise a novel inverted indexing scheme, based on Bloom filters, to scalably index high order features extracted from pairs of nearby features. Unlike a conventional inverted index, we can adapt the size of the inverted index to maintain adequate sparsity of the posting lists. This ensures constant time query retrievals. We are thus able to implement an exhaustive pairwise matching scheme, with linear time complexity, using the ‘query each image in turn’ technique. We find the exhaustive nature of our approach to be very useful in mining small clusters of images, as demonstrated by a 73.2% recall on the UKBench dataset. In the Oxford Buildings dataset, we are able to discover all the query buildings. We also discover interesting overlapping images connecting distant images.


  1. 1.
    Li, Y., Snavely, N., Huttenlocher, D.P.: Location Recognition Using Prioritized Feature Matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., Guibas, L.J.: Image webs: Computing and exploiting connectivity in image collections. In: CVPR, pp. 3432–3439 (2010)Google Scholar
  3. 3.
    Simon, I., Snavely, N., Seitz, S.M.: Scene summarization for online image collections. In: ICCV, pp. 1–8 (2007)Google Scholar
  4. 4.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25(3), 835–846 (2006)CrossRefGoogle Scholar
  5. 5.
    Srijan, K., Ishtiaque, S.A., Sinha, S., Jawahar, C.V.: Image-based walkthroughs from incremental and partial scene reconstructions. In: BMVC (2010)Google Scholar
  6. 6.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR, pp. 809–816 (2011)Google Scholar
  10. 10.
    Wang, X., Yang, M., Cour, T., Zhu, S., Yu, K., Han, T.X.: Contextual weighting for vocabulary tree based image retrieval. In: ICCV, pp. 209–216 (2011)Google Scholar
  11. 11.
    Chum, O., Perdoch, M., Matas, J.: Geometric min-hashing: Finding a (thick) needle in a haystack. In: CVPR, pp. 17–24 (2009)Google Scholar
  12. 12.
    Philbin, J., Zisserman, A.: Object mining using a matching graph on very large image collections. In: ICVGIP, pp. 738–745 (2008)Google Scholar
  13. 13.
    Chum, O., Matas, J.: Large-scale discovery of spatially related images. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 371–377 (2010)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Chen, T.: Efficient kernels for identifying unbounded-order spatial features. In: CVPR, pp. 1762–1769 (2009)Google Scholar
  15. 15.
    Xu, Y., Madison, R.: Robust object recognition using a cascade of geometric consistency filters. In: AIPR 2009, pp. 1–8 (2009)Google Scholar
  16. 16.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)zbMATHCrossRefGoogle Scholar
  17. 17.
    Mitzenmacher, M.: Compressed bloom filters. IEEE/ACM Trans. Netw. 10(5), 604–612 (2002)CrossRefGoogle Scholar
  18. 18.
    Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2), pp. 2161–2168 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Center for Visual Information TechnologyIIITHyderabadIndia

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