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Robust Duplicate Detection of 2D and 3D Objects

Author

Listed:
  • Peter Vajda

    (Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)

  • Ivan Ivanov

    (Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)

  • Lutz Goldmann

    (Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)

  • Jong-Seok Lee

    (Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)

  • Touradj Ebrahimi

    (Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)

Abstract

In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of object duplicate detection algorithm is measured over different object classes. The authors’ method is shown to be robust in detecting the same objects even when images with objects are taken from different viewpoints or distances.

Suggested Citation

  • Peter Vajda & Ivan Ivanov & Lutz Goldmann & Jong-Seok Lee & Touradj Ebrahimi, 2010. "Robust Duplicate Detection of 2D and 3D Objects," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 1(3), pages 19-40, July.
    • Handle: RePEc:igg:jmdem0:v:1:y:2010:i:3:p:19-40
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