Properties of Patch Based Approaches for the Recognition of Visual Object Classes

  • Alexandra Teynor
  • Esa Rahtu
  • Lokesh Setia
  • Hans Burkhardt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Patch based approaches have recently shown promising results for the recognition of visual object classes. This paper investigates the role of different properties of patches. In particular, we explore how size, location and nature of interest points influence recognition performance. Also, different feature types are evaluated. For our experiments we use three common databases at different levels of difficulty to make our statements more general. The insights given in the conclusion can serve as guidelines for developers of algorithms using image patches.


Patch Size Image Retrieval Interest Point Image Patch Object Categorization 
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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  1. 1.
    Santini, S., Gupta, A., Smeulders, A., Worring, M., Jain, R.: Content based image retrieval at the end of the early years  22, 1349–1380 (2000)Google Scholar
  2. 2.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. IJCV 37, 151–172 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Wallraven, C., Caputo, B., Graf, A.: Recognition with local features: the kernel recipe. In: Proc. ICCV (2003)Google Scholar
  4. 4.
    Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE TPAMI 26, 1475–1490 (2004)Google Scholar
  5. 5.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. CVPR, Madison, WI, vol. 2, pp. 264–271 (2003)Google Scholar
  6. 6.
    Schmid, C., Mohr, R.: Local greyvalue invariants for image retrieval. IEEE TPAMI 19, 530–535 (1997)Google Scholar
  7. 7.
    Weber, M., Welling, M., Perona, P.: Unsupervised Learning of Models for Recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Leibe, B., Schiele, B.: Interleaved object categorization and segmentation. In: Proc. BMVC, Norwich, UK (2003)Google Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Deselaers, T., Keysers, D., Ney, H.: Discriminative training for object recognition using image patches. In: Proc. CVPR, San Diego, CA, vol. 2, pp. 157–162 (2005)Google Scholar
  11. 11.
    Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE TPAMI 28, 416–431 (2006)Google Scholar
  12. 12.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE TPAMI 27, 1615–1630 (2005)Google Scholar
  13. 13.
    Mikolajczyk, K., Leibe, B., Schiele, B.: Local features for object class recognition. In: Proc. ICCV, vol. 2, pp. 1792–1799 (2005)Google Scholar
  14. 14.
    Sebe, N., Lew, M.S.: Comparing salient point detectors. PR Letters 24, 89–96 (2003)zbMATHGoogle Scholar
  15. 15.
    Loupias, E., Sebe, N.: Wavelet based salient points for image retrieval. Technical report, Laboratoire Reconnaissance de Formes et Vision, INSA Lyon (1999)Google Scholar
  16. 16.
    Halawani, A., Burkhardt, H.: Image retrieval by local evaluation of nonlinear kernel functions around salient points. In: Proc. ICPR (2004)Google Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 60, 63–86 (2004)CrossRefGoogle Scholar
  18. 18.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. BMVC, Cardif, UK (2002)Google Scholar
  19. 19.
    Rahtu, E., Salo, M., Heikkilä, J.: Affine invariant pattern recognition using multiscale autoconvolution. IEEE TPAMI 27, 908–918 (2005)Google Scholar
  20. 20.
    Schulz-Mirbach, H.: Anwendung von Invarianzprinzipien zur Merkmalgewinnung. PhD thesis, TU Hamburg-Harburg, Reihe 10, Nr. 372. VDI-Verlag (1995)Google Scholar
  21. 21.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. ICCV, Corfu, Greece, pp. 1150–1157 (1999)Google Scholar
  22. 22.
    Leibe, B., Schiele, B.: Analyzing contour and appearance based methods for object categorization. In: Proc. CVPR, Madison, WI (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alexandra Teynor
    • 1
  • Esa Rahtu
    • 2
  • Lokesh Setia
    • 1
  • Hans Burkhardt
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.Department of Electrical and Information EngineeringUniversity of OuluOuluFinland

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