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

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Part of the Lecture Notes in Computer Science book series (LNIP,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. Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  3. Jegou, H., Schmid, C., Harzallah, H., Verbeek, J.: Accurate image search using the contextual dissimilarity measure. PAMI 32, 2–11 (2009)

    CrossRef  Google Scholar 

  4. Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR (2007)

    Google Scholar 

  5. Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)

    Google Scholar 

  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. Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: ECCV (2010)

    Google Scholar 

  8. Inria, searching for similar images in a database of 10 million images - example queries,

  9. Google Goggles,

  10. Google. Search By Image,

  11. Baidu. Search by Image,

  12. Sogou. Search by Image,

  13. TinEye blog,

  14. Turtle, H., Flood, J.: Query evaluation: strategies and optimizations. Information Processing & Management 31, 831–850 (1995)

    CrossRef  Google Scholar 

  15. Kaszkiel, M., Zobel, J., Sacks-davis, R.: Efficient passage ranking for document databases. ACM Transactions on Information Systems 17, 406–439 (1999)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  17. Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  19. Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR (2011)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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)

    MathSciNet  CrossRef  Google Scholar 

  23. Knuth, D.E.: The Art of Computer Programming, vol. III: Sorting and Searching, 2nd edn. (1998)

    Google Scholar 

  24. Fraundorfer, F., Stewénius, H., Nistér, D.: A binning scheme for fast hard drive based image search. In: CVPR (2007)

    Google Scholar 

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Stewénius, H., Gunderson, S.H., Pilet, J. (2012). Size Matters: Exhaustive Geometric Verification for Image Retrieval Accepted for ECCV 2012. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7573. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33708-6

  • Online ISBN: 978-3-642-33709-3

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