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Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11364))

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Abstract

Despite its complexity, the minimum cost lifted multicut problem has found a wide range of applications in recent years, such as image and mesh decomposition or multiple object tracking. Its solutions are decompositions of a graph into an optimal number of segments which are optimized w.r.t. a cost function defined on a superset of the edge set. While the currently available solvers for this problem provide high quality solutions in terms of the task to be solved, they can have long computation times for more difficult problem instances. Here, we propose two variants of a heuristic solver (primal feasible heuristic), which greedily generate solutions within a bounded amount of time. Evaluations on image and mesh segmentation benchmarks show the high quality of these solutions.

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Notes

  1. 1.

    https://www.mpi-inf.mpg.de/fileadmin/inf/d2/levinkov/iccv-2015/code.tar.gz.

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Acknowledgements

We acknowledge funding by the DFG project KE 2264/1-1. We also acknowledge the NVIDIA Corporation for the donation of a Titan Xp GPU.

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Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Amirhossein Kardoost & Margret Keuper

Authors
  1. Amirhossein Kardoost
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  2. Margret Keuper
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Corresponding author

Correspondence to Amirhossein Kardoost .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Kardoost, A., Keuper, M. (2019). Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-20870-7_5

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  • Print ISBN: 978-3-030-20869-1

  • Online ISBN: 978-3-030-20870-7

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