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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 2013: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 321–334Cite as

Variational Image Segmentation and Cosegmentation with the Wasserstein Distance

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8081))

Abstract

We present novel variational approaches for segmenting and cosegmenting images. Our supervised segmentation approach extends the classical Continuous Cut approach by a global appearance-based data term enforcing closeness of aggregated appearance statistics to a given prior model. This novel data term considers non-spatial, deformation-invariant statistics with the help of the Wasserstein distance in a single global model. The unsupervised cosegmentation model also employs the Wasserstein distance for finding the common object in two images. We introduce tight convex relaxations for both presented models together with efficient algorithmic schemes for computing global minimizers. Numerical experiments demonstrate the effectiveness of our models and the convex relaxations.

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Author information

Authors and Affiliations

  1. Image and Pattern Analysis Group & HCI, Dept. of Mathematics and Computer Science, University of Heidelberg, Germany

    Paul Swoboda & Christoph Schnörr

Authors
  1. Paul Swoboda
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  2. Christoph Schnörr
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Editor information

Editors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Sweden

    Anders Heyden , Fredrik Kahl , Carl Olsson  & Magnus Oskarsson , ,  & 

  2. Dept. of Mathematics, University of Bergen, Johaness Brunsgate 12, 5007, Bergen, Norway

    Xue-Cheng Tai

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Swoboda, P., Schnörr, C. (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, XC. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2013. Lecture Notes in Computer Science, vol 8081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40395-8_24

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  • DOI: https://doi.org/10.1007/978-3-642-40395-8_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40394-1

  • Online ISBN: 978-3-642-40395-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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