Region Graphs for Organizing Image Collections

  • Alexander Ladikos
  • Edmond Boyer
  • Nassir Navab
  • Slobodan Ilic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


In this paper we consider large image collections and their organization into meaningful data structures upon which applications can be build (e.g. navigation or reconstruction). In contrast to structures that only reflect local relationships between pairs of images we propose to account for the information an image brings to a collection with respect to all other images. Our approach builds on abstracting from image domains and focusing on image regions, thereby reducing the influence of outliers and background clutter. We introduce a graph structure based on these regions which encodes the overlap between them. The contribution of an image to a collection is then related to the amount of overlap of its regions with the other images in the collection. We demonstrate our graph based structure with several applications: image set reduction, canonical view selection and image-based navigation. The data sets used in our experiments range from small examples to large image collections with thousands of images.


Convex Hull Edge Weight Graph Construction Image Collection Region Graph 
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.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. International Journal of Computer Vision 80, 189–210 (2008)CrossRefGoogle Scholar
  2. 2.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a day. In: International Conference on Computer Vision (2009)Google Scholar
  3. 3.
    Simon, I., Snavely, N., Seitz, S.M.: Scene summarization for online image collections. In: International Conference on Computer Vision (2007)Google Scholar
  4. 4.
    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
  5. 5.
    Snavely, N., Garg, R., Seitz, S.M., Szeliski, R.: Finding paths through the world’s photos. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2008) 27, 11–21 (2008)Google Scholar
  6. 6.
    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
  7. 7.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  8. 8.
    Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal graphs for efficient structure from motion. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  9. 9.
    Whyte, O., Sivic, J., Zisserman, A.: Get out of my picture! internet-based inpainting. In: British Machine Vision Conference (2009)Google Scholar
  10. 10.
    Farenzena, M., Fusiello, A., Gherardi, R.: Structure-and-motion piepline on a hierarchical cluster tree. In: IEEE International Workshop on 3-D Digital Imaging and Modeling (2009)Google Scholar
  11. 11.
    Schaffalitzky, F., Zisserman, A.: Multi-view Matching for Unordered Image Sets, or How Do I Organize My Holiday Snaps? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 414–431. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  14. 14.
    Fraundorfer, F., Wu, C., Frahm, J.-M., Pollefeys, M.: Visual word based location recognition in 3D models using distance augmented weighting. In: Fourth International Symposium on 3D Data Processing, Visualization and Transmission (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexander Ladikos
    • 1
  • Edmond Boyer
    • 2
  • Nassir Navab
    • 1
  • Slobodan Ilic
    • 1
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.Perception TeamINRIA Grenoble Rhône-AlpesFrance

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