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Maximizing Flows with Message-Passing: Computing Spatially Continuous Min-Cuts

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8932))

Abstract

In this work, we study the problems of computing spatially continuous cuts, which has many important applications of image processing and computer vision. We focus on the convex relaxed formulations and investigate the corresponding flow-maximization based dual formulations. We propose a series of novel continuous max-flow models based on evaluating different constraints of flow excess, where the classical pre-flow and pseudo-flow models over graphs are re-discovered in the continuous setting and re-interpreted in a new variational manner. We propose a new generalized proximal method, which is based on a specific entropic distance function, to compute the maximum flow. This leads to new algorithms exploring flow-maximization and message-passing simultaneously. We show the proposed algorithms are superior to state of art methods in terms of efficiency.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of California, Los Angeles, USA

    Egil Bae

  2. Department of Medical Biophysics, Schulich Medical School, Western University, Canada

    Jing Yuan

  3. Department of Mathematics, University of Bergen, Norway

    Xue-Cheng Tai

Authors
  1. Egil Bae
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  2. Xue-Cheng Tai
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  3. Jing Yuan
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, 7800, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Mathematics, University of California, Los Angeles, CA, USA

    Egil Bae

  3. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, S.A.R.

    Tony F. Chan

  4. Telemark University College, Postboks 203, 3901, Porsgrunn, Norway

    Marius Lysaker

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Bae, E., Tai, XC., Yuan, J. (2015). Maximizing Flows with Message-Passing: Computing Spatially Continuous Min-Cuts. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-14612-6_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14611-9

  • Online ISBN: 978-3-319-14612-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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