Fast Organization of Large Photo Collections Using CUDA

  • Tim Johnson
  • Pierre Fite-Georgel
  • Rahul Raguram
  • Jan-Michael Frahm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


In this paper, we introduce a system for the automatic organization of photo collections consisting of millions of images downloaded from the Internet. To our knowledge, this is the first approach that tackles this problem exclusively through the use of general-purpose GPU computing techniques. By leveraging the inherent parallelism of the problem and through the use of efficient GPU-based algorithms, our system is able to effectively summarize datasets containing up to three million images in approximately 16 hours on a single PC, which is orders of magnitude faster compared to current state of the art techniques. In this paper, we present the various algorithmic considerations and design aspects of our system, and describe in detail the various steps of the processing pipeline. Additionally, we demonstrate the effectiveness of the system by showing results for a variety of real-world datasets, ranging from the scale of a single landmark, to that of an entire city.


Binary Code Fundamental Matrix Query Expansion Thread Block Virtual 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.


  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a day. In: ICCV (2009)Google Scholar
  6. 6.
    Berg, T.L., Berg, A.C.: Finding iconic images. In: The 2nd Internet Vision Workshop at IEEE CVPR (2009)Google Scholar
  7. 7.
    Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal sets for efficient structure from motion. In: CVPR (2008)Google Scholar
  8. 8.
    Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. JACM 45, 891–923 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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
  10. 10.
    Strong, G., Gong, M.: Browsing a Large Collection of Community Photos Based on Similarity on GPU. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 390–399. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Strong, G., Gong, M.: Organizing and browsing photos using different feature vectors and their evaluations. In: CIVR, pp. 1–8 (2009)Google Scholar
  12. 12.
    Wang, Y., Feng, Z., Guo, H., He, C., Yang, Y.: Scene recognition acceleration using cuda and openmp. In: ICISE, pp. 1422–1425 (2009)Google Scholar
  13. 13.
    Shalom, S.A.A., Dash, M., Tue, M.: Efficient K-Means Clustering Using Accelerated Graphics Processors. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 166–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Hall, J.D., Hart, J.C.: Abstract gpu acceleration of iterative clustering (2004)Google Scholar
  15. 15.
    Kennedy, L., Chang, S.F., Kozintsev, I.: To search or to label?: Predicting the performance of search-based automatic image classifiers. ACM MIR (2006)Google Scholar
  16. 16.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hays, J., Efros, A.A.: Scene completion using millions of photographs. SIGGRAPH (2007)Google Scholar
  18. 18.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data An Introduction to Cluster Analysis. Wiley Interscience, New York (1990)CrossRefGoogle Scholar
  19. 19.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  20. 20.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24 (1981)Google Scholar
  21. 21.
    Beardsley, P., Zisserman, A., Murray, D.: Sequential updating of projective and affine structure from motion. Int. J. Computer Vision 23, 235–259 (1997)CrossRefGoogle Scholar
  22. 22.
    Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. Journal of Field Robotics 23 (2006)Google Scholar
  23. 23.
    Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. NIPS 22, 1509–1517 (2009)Google Scholar
  24. 24.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518 Google Scholar
  25. 25.
    Raguram, R., Frahm, J.-M., Pollefeys, M.: A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 500–513. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tim Johnson
    • 1
  • Pierre Fite-Georgel
    • 1
  • Rahul Raguram
    • 1
  • Jan-Michael Frahm
    • 1
  1. 1.Department of Computer ScienceUniversity of North Carolina at Chapel HillUSA

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