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Updated: Oct 4, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation.

Evgeny Levinkov, Amirhossein Kardoost, Bjoern Andres

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 7, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel method for graph partitioning using higher-order costs, enabling more flexible multiple model fitting. The efficient local search algorithm effectively handles complex optimization problems.

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    Area of Science:

    • Computer Science
    • Graph Theory
    • Optimization

    Background:

    • The minimum cost lifted multicut problem generalizes graph partitioning with positive and negative edge costs.
    • Standard multicut formulations are limited to pairwise relationships, hindering applications requiring higher-order interactions (hyper-edges).

    Purpose of the Study:

    • To propose a pseudo-boolean formulation for higher-order minimum cost lifted multicuts.
    • To enable multiple model fitting by partitioning graphs with costs defined over hyper-edges.

    Main Methods:

    • Developed a pseudo-boolean formulation for any-order minimum cost lifted multicuts.
    • Proposed an efficient local search algorithm for inference due to the NP-hard nature of the formulation.

    Main Results:

    • The formulation allows partitioning undirected graphs to minimize costs over hyper-edges.
    • The local search algorithm demonstrates effectiveness and versatility in various applications.

    Conclusions:

    • The proposed approach extends multicut formulations to handle higher-order costs for improved multiple model fitting.
    • The efficient local search algorithm provides a practical solution for complex graph partitioning problems.