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Universal Seed Skin Segmentation

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Advances in Visual Computing (ISVC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6454))

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Abstract

We present a principled approach for general skin segmentation using graph cuts. We present the idea of a highly adaptive universal seed thereby exploiting the positive training data only. We model the skin segmentation as a min-cut problem on a graph defined by the image color characteristics. The prior graph cuts based approaches for skin segmentation do not provide general skin detection when the information of foreground or background seeds is not available. We propose a concept for processing arbitrary images; using a universal seed to overcome the potential lack of successful seed detections thereby providing basis for general skin segmentation. The advantage of the proposed approach is that it is based on skin sampled training data only making it robust to unseen backgrounds. It exploits the spatial relationship among the neighboring skin pixels providing more accurate and stable skin blobs. Extensive evaluation on a dataset of 8991 images with annotated pixel-level ground truth show that the universal seed approach outperforms other state of the art approaches.

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Author information

Authors and Affiliations

  1. Computer Vision Lab, Vienna University of Technology, Vienna, Austria

    Rehanullah Khan & Julian Stöttinger

  2. Cog Vis Ltd., Vienna, Austria

    Julian Stöttinger

  3. Information Retrieval Facility, Vienna, Austria

    Allan Hanbury

Authors
  1. Rehanullah Khan
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  2. Allan Hanbury
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  3. Julian Stöttinger
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. Moffett Field, NASA Ames Research Center, 94035, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammound

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

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© 2010 Springer-Verlag Berlin Heidelberg

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Khan, R., Hanbury, A., Stöttinger, J. (2010). Universal Seed Skin Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-17274-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17273-1

  • Online ISBN: 978-3-642-17274-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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