# 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. Expand

## 36 Citations

Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration

- Computer ScienceACCV
- 2018

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.

- Computer ScienceIEEE transactions on pattern analysis and machine intelligence
- 2022

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

- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2021

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

- Computer Science2017 IEEE International Conference on Computer Vision (ICCV)
- 2017

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

- Computer Science2020 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

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

- Computer ScienceECCV
- 2018

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

- Computer ScienceArXiv
- 2019

The Benders decomposition approach provides a promising new avenue to accelerate optimization for CC, and allows for massive parallelization.

Analysis and Optimization of Graph Decompositions by Lifted Multicuts

- MathematicsICML
- 2017

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

- Computer ScienceArXiv
- 2022

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.

Efficient Algorithms for Moral Lineage Tracing

- Computer Science2017 IEEE International Conference on Computer Vision (ICCV)
- 2017

This work devise the first efficient primal feasible local search algorithms for the moral lineage tracing problem (MLTP) and shows in experiments that these algorithms find accurate solutions on the problem instances of Jug et al. and scale to larger instances.

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