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An Evaluation of Two Automatic Landmark Building Discovery Algorithms for City Reconstruction

  • Tobias Weyand
  • Jan Hosang
  • Bastian Leibe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)

Abstract

An important part of large-scale city reconstruction systems is an image clustering algorithm that divides a set of images into groups that should cover only one building each. Those groups then serve as input for structure from motion systems. A variety of approaches for this mining step have been proposed recently, but there is a lack of comparative evaluations and realistic benchmarks. In this work, we want to fill this gap by comparing two state-of-the-art landmark mining algorithms: spectral clustering and min-hash. Furthermore, we introduce a new large-scale dataset for the evaluation of landmark mining algorithms consisting of 500k images from the inner city of Paris. We evaluate both algorithms on the well-known Oxford dataset and our Paris dataset and give a detailed comparison of the clustering quality and computation time of the algorithms.

Keywords

Ground Truth Visual Word Spectral Cluster Query Expansion Seed Image 
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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References

  1. 1.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a Day. In: ICCV 2009 (2009)Google Scholar
  2. 2.
    Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.-M.: Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 427–440. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Snavely, N., Seitz, S., Szeliski, R.: Modeling the World from Internet Photo Collections. IJCV 80, 189–210 (2008)CrossRefGoogle Scholar
  4. 4.
    Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.: Multi-View Stereo for Community Photo Collections. In: ICCV 2007 (2007)Google Scholar
  5. 5.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Manhattan-world stereo. In: CVPR 2009, IEEE (2009)Google Scholar
  6. 6.
    Snavely, N., Seitz, S., Szeliski, R.: Photo Tourism: Exploring Photo Collections in 3D. In: SIGGRAPH 2006 (2006)Google Scholar
  7. 7.
    Gammeter, S., Quack, T., Van Gool, L.: I Know What You Did Last Summer: Object-Level Auto-Annotation of Holiday Snaps. In: ICCV 2009 (2009)Google Scholar
  8. 8.
    Simon, I., Seitz, S.M.: Scene Segmentation Using the Wisdom of Crowds. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 541–553. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Simon, I., Snavely, N., Seitz, S.: Scene Summarization for Online Image Collections. In: ICCV 2007 (2007)Google Scholar
  10. 10.
    Quack, T., Leibe, B., Van Gool, L.: World-Scale Mining of Objects and Events from Community Photo Collections. In: CIVR 2008 (2008)Google Scholar
  11. 11.
    Philbin, J., Zisserman, A.: Object Mining using a Matching Graph on Very Large Image Collections. In: ICCVGIP 2008 (2008)Google Scholar
  12. 12.
    Chum, O., Matas, J.: Large-scale discovery of spatially related images. In: PAMI (2010)Google Scholar
  13. 13.
    Chum, O., Perdoch, M., Matas, J.: Geometric min-Hashing: Finding a (Thick) Needle in a Haystack. In: ICCV 2007 (2007)Google Scholar
  14. 14.
    Chum, O., Philbin, J., Sivic, J., Zisserman, A.: Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval. In: ICCV 2007 (2007)Google Scholar
  15. 15.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object Retrieval with Large Vocabularies and Fast Spatial Matching. In: CVPR 2007 (2007)Google Scholar
  16. 16.
    Strecha, C., Pylvanainen, T., Fua, P.: Dynamic and Scalable Large Scale Image Reconstruction. In: CVPR 2010 (2010)Google Scholar
  17. 17.
    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 (2009)Google Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 60, 63–86 (2004)CrossRefGoogle Scholar
  19. 19.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 60 (2004)Google Scholar
  20. 20.
    Sattler, T., Leibe, B., Kobbelt, L.: SCRAMSAC: Improving RANSAC’s Efficiency with a Spatial Consistency Filter. In: ICCV 2009 (2009)Google Scholar
  21. 21.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS 2001. MIT Press (2001)Google Scholar
  22. 22.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69 (2004)Google Scholar
  23. 23.
    Chum, O., Philbin, J., Zisserman, A.: Near Duplicate Image Detection: min-Hash and tf-idf Weighting. In: BMVC 2008 (2008)Google Scholar
  24. 24.
    Broder, A.: On the resemblance and containment of documents. In: SEQS 1997 (1997)Google Scholar
  25. 25.
    Chum, O., Philbin, J., Isard, M., Zisserman, A.: Scalable near identical image and shot detection. In: CIVR 2007 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tobias Weyand
    • 1
  • Jan Hosang
    • 1
  • Bastian Leibe
    • 1
  1. 1.UMIC Research CentreRWTH Aachen UniversityGermany

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