Exemplar Based Recognition of Visual Shapes

  • Søren I. Olsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints. The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.


Recognition Rate Training Image Semantic Content Query Image Background Clutter 
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.
    Agarwal, S., Awan, A., Roth, D.: UIUC Image Database for Car Detection,
  2. 2.
    Heitger, F., Rosenthaler, L., Von der Heydt, R., Peterhans, E., Kubler, O.: Simulation of Neural Contour Mechanisms: from Simple to End-stopped Cells. Vision Research 32(5), 963–981 (1992)CrossRefGoogle Scholar
  3. 3.
    Lowe, D.: Object Recognition from Local Scale-Invariant Features. In: Proc. 7’th ICCV, pp. 1150–1157 (1999)Google Scholar
  4. 4.
    Lowe, D.: Local feature view clustering for 3D object recognition. In: IEEE Conf. on Computer Vision and Pattern Recognition, Hawaii, pp. 682–688 (2001)Google Scholar
  5. 5.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Jour. of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. Int. Jour. of Computer Vision 60(1), 63–86 (2004)CrossRefGoogle Scholar
  7. 7.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (1996),
  8. 8.
    Olsen, S.I.: End-Stop Exemplar based Recognition. In: Proceedings of the 13th Scandinavian Conference on Image Analysis, pp. 43–50 (2003)Google Scholar
  9. 9.
    Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. IEEE trans. PAMI 19(5), 530–535 (1997)Google Scholar
  10. 10.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of Interest Point Detectors. Int. Jour. of Computer Vision 37(2), 151–172 (2000)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Søren I. Olsen
    • 1
  1. 1.Department of Computer ScienceUniversity of CopenhagenDenmark

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