Reducing Keypoint Database Size

  • Shahar Jamshy
  • Eyal Krupka
  • Yehezkel Yeshurun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Keypoints are high dimensional descriptors for local features of an image or an object. Keypoint extraction is the first task in various computer vision algorithms, where the keypoints are then stored in a database used as the basis for comparing images or image features. Keypoints may be based on image features extracted by feature detection operators or on a dense grid of features. Both ways produce a large number of features per image, causing both time and space performance challenges when upscaling the problem.

We propose a novel framework for reducing the size of the keypoint database by learning which keypoints are beneficial for a specific application and using this knowledge to filter out a large portion of the keypoints. We demonstrate this approach on an object recognition application that uses a keypoint database. By using leave one out K nearest neighbor regression we significantly reduce the number of keypoints with relatively small reduction in performance.


Keypoints Saliency Recognition ALOI 


  1. 1.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, pp. 1150–1157 (1999)Google Scholar
  2. 2.
    Obdrzálek, S., Matas, J.: Object recognition using local affine frames on distinguished regions. In: Rosin, P.L., Marshall, A.D. (eds.) BMVC, British Machine Vision Association (2002)Google Scholar
  3. 3.
    Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  4. 4.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR, pp. 264–271. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  5. 5.
    Tissainayagam, P., Suter, D.: Object tracking in image sequences using point features. In: APRS Workshop on Digital Image Computing Online Proceedings, pp. 1197–1203 (2003)Google Scholar
  6. 6.
    Pritchett, P., Zisserman, A.: Wide baseline stereo matching. In: ICCV, pp. 754–760 (1998)Google Scholar
  7. 7.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Rosin, P.L., Marshall, D. (eds.) Proceedings of the British Machine Vision Conference, London, UK, September 2002, vol. 1, pp. 384–393. BMVA (2002)Google Scholar
  8. 8.
    Harris, C., Stephans, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., Manchester, August 1988, pp. 189–192 (1988)Google Scholar
  9. 9.
    Reisfeld, D., Wolfson, H., Yeshurun, Y.: Detection of interest points using symmetry. In: ICCV, pp. 62–65 (1990)Google Scholar
  10. 10.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 959–973 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Tankus, A., Yeshurun, Y., Intrator, N.: Face detection by direct convexity estimation. Pattern Recognition Letters 18(9), 913–922 (1997)CrossRefGoogle Scholar
  12. 12.
    Tankus, A., Yeshurun, Y.: Convexity-based visual camouflage breaking. Computer Vision and Image Understanding 82(3), 208–237 (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Bay, H., Tuytelaars, T., Gool, L.J.V.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Winder, S.A.J., Brown, M.: Learning local image descriptors. In: CVPR. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  19. 19.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.J.V.: A comparison of affine region detectors. International Journal of Computer Vision 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  20. 20.
    Ramisa, A., de Mántaras, R.L., Aldavert, D., Toledo, R.: Comparing combinations of feature regions for panoramic VSLAM. In: Zaytoon, J., Ferrier, J.-L., Andrade-Cetto, J., Filipe, J. (eds.) ICINCO-RA (2), pp. 292–297. INSTICC Press (2007)Google Scholar
  21. 21.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Anal. Mach. Intell. 30(4), 712–727 (2008)CrossRefGoogle Scholar
  23. 23.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vis. 61(1), 103–112 (2005)CrossRefGoogle Scholar
  24. 24.
    Litzkow, M., Livny, M., Mutka, M.: Condor - a hunter of idle workstations. In: Proceedings of the 8th International Conference of Distributed Computing Systems (June 1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shahar Jamshy
    • 1
  • Eyal Krupka
    • 2
  • Yehezkel Yeshurun
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Israel Innovation LabsMicrosoft Israel R&D CenterIsrael

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