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

  • Egil Bae
  • Xue-Cheng Tai
  • Jing Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8932)


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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Egil Bae
    • 1
  • Xue-Cheng Tai
    • 3
  • Jing Yuan
    • 2
  1. 1.Department of MathematicsUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Medical Biophysics, Schulich Medical SchoolWestern UniversityCanada
  3. 3.Department of MathematicsUniversity of BergenNorway

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