MatchMiner: Efficient Spanning Structure Mining in Large Image Collections

  • Yin Lou
  • Noah Snavely
  • Johannes Gehrke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)


Many new computer vision applications are utilizing large-scale data- sets of places derived from the many billions of photos on the Web. Such applications often require knowledge of the visual connectivity structure of these image collections—describing which images overlap or are otherwise related—and an important step in understanding this structure is to identify connected components of this underlying image graph. As the structure of this graph is often initially unknown, this problem can be posed as one of exploring the connectivity between images as quickly as possible, by intelligently selecting a subset of image pairs for feature matching and geometric verification, without having to test all O(n2) possible pairs. We propose a novel, scalable algorithm called MatchMiner that efficiently explores visual relations between images, incorporating ideas from relevance feedback to improve decision making over time, as well as a simple yet effective rank distance measure for detecting outlier images. Using these ideas, our algorithm automatically prioritizes image pairs that can potentially connect or contribute to large connected components, using an information-theoretic algorithm to decide which image pairs to test next. Our experimental results show that MatchMiner can efficiently find connected components in large image collections, significantly outperforming state-of-the-art image matching methods.


Mutual Information Visual Word Image Pair Query Image Relevance Feedback 
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.


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  1. 1.
    Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal graphs for efficient structure from motion. In: CVPR (2008)Google Scholar
  2. 2.
    Avidan, S., Moses, Y., Moses, Y.: Probabilistic Multi-view Correspondence in a Distributed Setting with No Central Server. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 428–441. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Guha, S., Khuller, S.: Approximation algorithms for connected dominating sets. Algorithmica 20, 374–387 (1998)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Frahm, J.-M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a Cloudless Day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25, 835–846 (2006)CrossRefGoogle Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60 (2004)Google Scholar
  7. 7.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2003)Google Scholar
  8. 8.
    Agarwal, S., Snavely, N., Simon, T., Seitz, S.M., Szeliski, R.: Building rome in a day. In: ICCV (2009)Google Scholar
  9. 9.
    Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., Guibas, L.J.: Image webs: Computing and exploiting connectivity in image collections. In: CVPR (2010)Google Scholar
  10. 10.
    Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)Google Scholar
  11. 11.
    Chum, O., Mikulík, A., Perdoch, M., Matas, J.: Total recall ii: Query expansion revisited. In: CVPR (2011)Google Scholar
  12. 12.
    Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: ICCV (2007)Google Scholar
  13. 13.
    Chum, O., Matas, J.: Large-scale discovery of spatially related images. IEEE Trans. Pattern Anal. Mach. Intell. 32, 371–377 (2010)CrossRefGoogle Scholar
  14. 14.
    Rocchio, J.J.: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall Inc. (1971)Google Scholar
  15. 15.
    Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: SIGIR (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yin Lou
    • 1
  • Noah Snavely
    • 1
  • Johannes Gehrke
    • 1
  1. 1.Department of Computer ScienceCornell UniversityUSA

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