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A Subjective Method for Image Segmentation Evaluation

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Computer Vision – ACCV 2009 (ACCV 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5996))

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Abstract

Image segmentation is an important processing step in many image understanding algorithms and practical vision systems. Various image segmentation algorithms have been proposed and most of them claim their superiority over others. But in fact, no general acceptance has been gained of the goodness of these algorithms. In this paper, we present a subjective method to assess the quality of image segmentation algorithms. Our method involves the collection of a set of images belonging to different categories, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and analyzing the final results. We outline the framework through an evaluation of four state-of-the-art image segmentation algorithms—mean-shift segmentation, JSEG, efficient graph based segmentation and statistical region merging, and give a detailed comparison of their different aspects.

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Author information

Authors and Affiliations

  1. Dept. of Automation, University of Science and Technology of China,  

    Qi Wang & Zengfu Wang

Authors
  1. Qi Wang
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  2. Zengfu Wang
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Editor information

Editors and Affiliations

  1. Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Hongbin Zha

  2. Department of Advanced Information Technology, Kyushu University, 819-0395, Fukuoka, Japan

    Rin-ichiro Taniguchi

  3. Birkbeck College, Department of Computer Science, University of London, WC1E 7HX, London, UK

    Stephen Maybank

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Wang, Q., Wang, Z. (2010). A Subjective Method for Image Segmentation Evaluation. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-12297-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12296-5

  • Online ISBN: 978-3-642-12297-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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