Size Matters: Exhaustive Geometric Verification for Image Retrieval Accepted for ECCV 2012

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)


The overreaching goals in large-scale image retrieval are bigger, better and cheaper. For systems based on local features we show how to get both efficient geometric verification of every match and unprecedented speed for the low sparsity situation.

Large-scale systems based on quantized local features usually process the index one term at a time, forcing two separate scoring steps: First, a scoring step to find candidates with enough matches, and then a geometric verification step where a subset of the candidates are checked.

Our method searches through the index a document at a time, verifying the geometry of every candidate in a single pass. We study the behavior of several algorithms with respect to index density—a key element for large-scale databases. In order to further improve the efficiency we also introduce a new new data structure, called the counting min-tree, which outperforms other approaches when working with low database density, a necessary condition for very large-scale systems.

We demonstrate the effectiveness of our approach with a proof of concept system that can match an image against a database of more than 90 billion images in just a few seconds.


Image Retrieval Query Image Inverted Index Size Matter Vocabulary Size 
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.


  1. 1.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)Google Scholar
  2. 2.
    Jegou, H., Douze, M., Schmid, C.: Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Jegou, H., Schmid, C., Harzallah, H., Verbeek, J.: Accurate image search using the contextual dissimilarity measure. PAMI 32, 2–11 (2009)CrossRefGoogle Scholar
  4. 4.
    Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR (2007)Google Scholar
  5. 5.
    Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)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.
    Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: ECCV (2010)Google Scholar
  8. 8.
    Inria, searching for similar images in a database of 10 million images - example queries,
  9. 9.
  10. 10.
  11. 11.
    Baidu. Search by Image,
  12. 12.
    Sogou. Search by Image,
  13. 13.
  14. 14.
    Turtle, H., Flood, J.: Query evaluation: strategies and optimizations. Information Processing & Management 31, 831–850 (1995)CrossRefGoogle Scholar
  15. 15.
    Kaszkiel, M., Zobel, J., Sacks-davis, R.: Efficient passage ranking for document databases. ACM Transactions on Information Systems 17, 406–439 (1999)CrossRefGoogle Scholar
  16. 16.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22, 761–767 (2004)CrossRefGoogle Scholar
  17. 17.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Lin, Z., Brandt, J.: A Local Bag-of-Features Model for Large-Scale Object Retrieval. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 294–308. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR (2011)Google Scholar
  20. 20.
    Mikolajczyk, K., Schmid, C.: An Affine Invariant Interest Point Detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Zheng, Y.T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.S., Neven, H.: Tour the world: Building a web-scale landmark recognition engine. In: CVPR (2009)Google Scholar
  22. 22.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Knuth, D.E.: The Art of Computer Programming, vol. III: Sorting and Searching, 2nd edn. (1998)Google Scholar
  24. 24.
    Fraundorfer, F., Stewénius, H., Nistér, D.: A binning scheme for fast hard drive based image search. In: CVPR (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.GoogleSwitzerland

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