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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3148795
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Levinkov, Evgeny
|e verfasserin
|4 aut
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| 245 |
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|a Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation
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| 264 |
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|c 2022
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 20.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a Minimum cost lifted multicut problem is a generalization of the multicut problem and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. Its main advantage is that multicut-based formulations do not require the number of components given a priori; instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e. hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is 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. As the proposed formulation is NP-hard and the branch-and-bound algorithm is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: geometric multiple model fitting, homography and motion estimation, motion segmentation
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Kardoost, Amirhossein
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Andres, Bjoern
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Keuper, Margret
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2022) vom: 07. Feb.
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:PP
|g year:2022
|g day:07
|g month:02
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|u http://dx.doi.org/10.1109/TPAMI.2022.3148795
|3 Volltext
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