Advertisement

Trademark Retrieval in the Presence of Occlusion

  • Dariusz Frejlichowski
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

Employing content based image retrieval (CBIR) methods to trademark registration can improve and accelerate the checking process greatly. Amongst all the features present in CBIR, shape seems to be the most appropriate for this task. It is however usually only utilized for non-occluded and noise free objects. In this paper the emphasis is put on the atypical case of the fraudulent creation of a new trademark based on a popular registered one. One can just modify an existing logo by, for example, removing or inserting a part into it. Another method is to modify even smaller subparts, which is close to adding noise to it’s silhouette. So, a method is herein described of template matching using a shape descriptor which is robust to rotation, scaling, shifting, and also to occlusion and noise.

Keywords

Machine Intelligence Shape Description Content Base Image Retrieval Correct Object Curvature Scale Space 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    1. Alfarez R., Wang Y.-F. (1999) Geometric and illumination invariants for object recognition, IEEE Trans. On Pattern Analysis and Machine Intelligence 21, 505–535CrossRefGoogle Scholar
  2. 2.
    2. Antani S., Kasturi R., Jain R. (2002) A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video, Pattern Recognition 35, 945–965zbMATHCrossRefGoogle Scholar
  3. 3.
    3. Belongie S., Malik J., Puzicha J. (2000) Shape context: a new descriptor for shape matching and object recognition, Proc. of Advances in Neural Information Processing Systems 13, 831–837Google Scholar
  4. 4.
    4. Bigun J., Bhattacharjee S.K., Michel S. (1996) Orientation radiograms for image retrieval: an alternative to segmentation, Proc. of the IEEE International Conference on Pattern Recognition, 346–350Google Scholar
  5. 5.
    5. Frejlichowski D. (2003) Problem braku czèsci sylwetki w rozpoznawaniu obrazów konturowych z uÿzyciem przekszta_lcenia do uk_ladu biegunowego, Materialy VIII Sesji Naukowej Informatyki, 181–188Google Scholar
  6. 6.
    6. Frejlichowski D. (2004) Metoda porównywania zniekszta_lconych dwuwymiarowych obiektów konturowych, Metody Informatyki Stosowanej w Technice i Technologii, 329–334Google Scholar
  7. 7.
    7. Huang C.-L., Huang D.-H. (1998) A content-based image retrieval system, Image and Vision Computing 16, 149–163CrossRefGoogle Scholar
  8. 8.
    8. Hupkens Th. M., Clippeleir J. de (1995) Noise and intensity invariant moments, Pattern Recognition Letters 16, 371–376CrossRefGoogle Scholar
  9. 9.
    9. Jin L., Tianxu Z. (2004) Fast algorithm for generation of moment invariants, Pattern Recognition 37, 1745–1756zbMATHGoogle Scholar
  10. 10.
    10. Kan C., Srinath M. D. (2002) Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments, Pattern Recognition 35, 143–154zbMATHCrossRefGoogle Scholar
  11. 11.
    11. Kuchariew G. (1998) Przetwarzanie i Analiza Obrazów Cyfrowych, Politechnika Szczecińska, Wydzia_l Informatyki, Szczecin, InformaGoogle Scholar
  12. 12.
    12. Loncaric S. (1998) A survey on shape analysis techniques, Pattern Recognition 31, 983–1001CrossRefGoogle Scholar
  13. 13.
    13. Mokhtarian F. (1997) Silhouette-based occluded object recognition through curvature scale space, Machine Vision and Applications 10, 87–97CrossRefGoogle Scholar
  14. 14.
    14. Rauber T.W. (1994) Two-dimensional shape description, Technical Report: GR UNINOVA-RT-10–94, Universidade Nova de LisboaGoogle Scholar
  15. 15.
    15. Rothe I., Süsse H., Voss K. (1996) The method of normalization to determine invariants, IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 366– 375CrossRefGoogle Scholar
  16. 16.
    16. Shih J. L., Chen L.-H. (2001) A new system for trademark segmentation and retrieval, Image and Vision Computing Journal 19, 1011–1018CrossRefGoogle Scholar
  17. 17.
    17. Tarel J.-P., Cooper D. B. (2000) The complex representation of algebraic curves and its simple exploitation for pose estimation and invariant recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 663–674CrossRefGoogle Scholar
  18. 18.
    18. Zhang D., Lu G. (2002) Shape-based image retrieval using Generic Fourier Descriptor, Signal Processing: Image Communication 17, 825–848CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Dariusz Frejlichowski
    • 1
  1. 1.Szczecin University of TechnologySzczecinPoland

Personalised recommendations