An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem

@inproceedings{Beier2016AnEF,
  title={An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem},
  author={Thorsten Beier and Bj{\"o}rn Andres and U. K{\"o}the and Fred A. Hamprecht},
  booktitle={ECCV},
  year={2016}
}
Many computer vision problems can be cast as an optimization problem whose feasible solutions are decompositions of a graph. [] Key Method We propose a fusion move algorithm for computing feasible solutions, better and more efficiently than existing algorithms. We demonstrate this and applications to image segmentation, obtaining a new state of the art for a problem in biological image analysis.
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37 Citations
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Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration
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This work proposes two variants of a heuristic solver (primal feasible heuristic), which greedily generate solutions within a bounded amount of time for the minimum cost lifted multicut problem.
Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation.
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This paper proposes a pseudo-boolean formulation for a multiple model fitting problem based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
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The Mutex Watershed is proposed, an efficient algorithm for graph partitioning that can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation
  • Margret Keuper
  • Computer Science
    2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
TLDR
This paper introduces a generalization of the minimum cost lifted multicut problem to hypergraphs, and proposes a simple primal feasible heuristic that allows for a reasonably efficient inference in instances of higher-order liftedMulticut problem instances defined on point trajectory hyper graphs for motion segmentation.
A Benders Decomposition Approach to Correlation Clustering
  • Jovita Lukasik, Margret Keuper, M. Singh, Julian Yarkony
  • Computer Science
    2020 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) and Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)
  • 2020
TLDR
The Benders decomposition approach provides a promising new avenue to accelerate optimization for CC, and, in contrast to previous cutting plane approaches, theoretically allows for massive parallelization.
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning
TLDR
When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives results that currently define the state-of-the-art in the competitive ISBI 2012 EM segmentation benchmark.
Massively Parallel Benders Decomposition for Correlation Clustering
TLDR
The Benders decomposition approach provides a promising new avenue to accelerate optimization for CC, and allows for massive parallelization.
RAMA: A Rapid Multicut Algorithm on GPU
TLDR
This work proposes a highly parallel primal-dual algorithm for the multicut problem, a classical graph clustering problem widely used in machine learning and computer vision, and shows resulting one to two orders-of-magnitudes improvements in execution speed without sac-rificing solution quality compared to traditional sequential algorithms that run on CPUs.
Analysis and Optimization of Graph Decompositions by Lifted Multicuts
TLDR
To find optimal decompositions defined by minimum cost lifted multicuts, this work establishes some properties of some facets of lifted multicut polytopes, define efficient separation procedures and apply these in a branch-and-cut algorithm.
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
TLDR
This work employs a reformulation of the multicut ILP constraints to a polynomial program as loss function that allows to learn feasible multicut solutions in a scalable way and provides the first approach towards end-to-end trainable multicuts.
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