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Towards Fully Automatic Image Segmentation Evaluation

  • Lutz Goldmann
  • Tomasz Adamek
  • Peter Vajda
  • Mustafa Karaman
  • Roland Mörzinger
  • Eric Galmar
  • Thomas Sikora
  • Noel E. O’Connor
  • Thien Ha-Minh
  • Touradj Ebrahimi
  • Peter Schallauer
  • Benoit Huet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)

Abstract

Spatial region (image) segmentation is a fundamental step for many computer vision applications. Although many methods have been proposed, less work has been done in developing suitable evaluation methodologies for comparing different approaches. The main problem of general purpose segmentation evaluation is the dilemma between objectivity and generality. Recently, figure ground segmentation evaluation has been proposed to solve this problem by defining an unambiguous ground truth using the most salient foreground object. Although the annotation of a single foreground object is less complex than the annotation of all regions within an image, it is still quite time consuming, especially for videos. A novel framework incorporating background subtraction for automatic ground truth generation and different foreground evaluation measures is proposed, that allows to effectively and efficiently evaluate the performance of image segmentation approaches. The experiments show that the objective measures are comparable to the subjective assessment and that there is only a slight difference between manually annotated and automatically generated ground truth.

Keywords

Ground Truth Image Segmentation Salient Object Foreground Object Segmentation Evaluation 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lutz Goldmann
    • 1
  • Tomasz Adamek
    • 2
  • Peter Vajda
    • 3
  • Mustafa Karaman
    • 1
  • Roland Mörzinger
    • 4
  • Eric Galmar
    • 5
  • Thomas Sikora
    • 1
  • Noel E. O’Connor
    • 2
  • Thien Ha-Minh
    • 3
  • Touradj Ebrahimi
    • 3
  • Peter Schallauer
    • 4
  • Benoit Huet
    • 5
  1. 1.Technical University Berlin (TUB)BerlinGermany
  2. 2.Dublin City University (DCU)DublinIreland
  3. 3.Ecole Polytechnique Federale de Lausanne (EPFL)LausanneSwitzerland
  4. 4.Joanneum Research (JRS)GrazAustria
  5. 5.Institut EurecomSophia-AntipolisFrance

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