---
_id: '7468'
abstract:
- lang: eng
text: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference
in structured energy minimization problems. The method optimizes a Lagrangean
relaxation of the original energy minimization problem using a multi plane block-coordinate
Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean
decomposition. We show empirically that our method outperforms state-of-the-art
Lagrangean decomposition based algorithms on some challenging Markov Random Field,
multi-label discrete tomography and graph matching problems.
article_number: 11138-11147
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. Vol 2019-June. IEEE; 2019. doi:10.1109/CVPR.2019.01140'
apa: 'Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate
Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA,
United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140'
chicago: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vol. 2019–June. IEEE, 2019. https://doi.org/10.1109/CVPR.2019.01140.
ieee: P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe
algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol.
2019–June.
ista: 'Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe
algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition
vol. 2019–June, 11138–11147.'
mla: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2019–June, 11138–11147, IEEE,
2019, doi:10.1109/CVPR.2019.01140.
short: P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2019.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2019-06-15
date_created: 2020-02-09T23:00:52Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2023-09-07T14:54:24Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/CVPR.2019.01140
ec_funded: 1
external_id:
arxiv:
- '1806.05049'
isi:
- '000542649304076'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.05049
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9781728132938'
issn:
- '10636919'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Map inference via block-coordinate Frank-Wolfe algorithm
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2019-June
year: '2019'
...
---
_id: '703'
abstract:
- lang: eng
text: We consider the NP-hard problem of MAP-inference for undirected discrete graphical
models. We propose a polynomial time and practically efficient algorithm for finding
a part of its optimal solution. Specifically, our algorithm marks some labels
of the considered graphical model either as (i) optimal, meaning that they belong
to all optimal solutions of the inference problem; (ii) non-optimal if they provably
do not belong to any solution. With access to an exact solver of a linear programming
relaxation to the MAP-inference problem, our algorithm marks the maximal possible
(in a specified sense) number of labels. We also present a version of the algorithm,
which has access to a suboptimal dual solver only and still can ensure the (non-)optimality
for the marked labels, although the overall number of the marked labels may decrease.
We propose an efficient implementation, which runs in time comparable to a single
run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art
results on computational benchmarks from machine learning and computer vision.
arxiv: 1
author:
- first_name: Alexander
full_name: Shekhovtsov, Alexander
last_name: Shekhovtsov
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 2018;40(7):1668-1682. doi:10.1109/TPAMI.2017.2730884
apa: Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency
via iterative relaxed inference with graphical models. IEEE Transactions on
Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884
chicago: Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum
Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2017.2730884.
ieee: A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative
relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
ista: Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 40(7), 1668–1682.
mla: Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference
with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:10.1109/TPAMI.2017.2730884.
short: A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern
Analysis and Machine Intelligence 40 (2018) 1668–1682.
date_created: 2018-12-11T11:48:01Z
date_published: 2018-07-01T00:00:00Z
date_updated: 2021-01-12T08:11:32Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/TPAMI.2017.2730884
external_id:
arxiv:
- '1508.07902'
intvolume: ' 40'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1508.07902
month: '07'
oa: 1
oa_version: Preprint
page: 1668-1682
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- '01628828'
publication_status: published
publisher: IEEE
publist_id: '6992'
quality_controlled: '1'
scopus_import: 1
status: public
title: Maximum persistency via iterative relaxed inference with graphical models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 40
year: '2018'
...
---
_id: '5978'
abstract:
- lang: eng
text: 'We consider the MAP-inference problem for graphical models,which is a valued
constraint satisfaction problem defined onreal numbers with a natural summation
operation. We proposea family of relaxations (different from the famous Sherali-Adams
hierarchy), which naturally define lower bounds for itsoptimum. This family always
contains a tight relaxation andwe give an algorithm able to find it and therefore,
solve theinitial non-relaxed NP-hard problem.The relaxations we consider decompose
the original probleminto two non-overlapping parts: an easy LP-tight part and
adifficult one. For the latter part a combinatorial solver must beused. As we
show in our experiments, in a number of applica-tions the second, difficult part
constitutes only a small fractionof the whole problem. This property allows to
significantlyreduce the computational time of the combinatorial solver andtherefore
solve problems which were out of reach before.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Stefan
full_name: Haller, Stefan
last_name: Haller
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: 'Haller S, Swoboda P, Savchynskyy B. Exact MAP-inference by confining combinatorial
search with LP relaxation. In: Proceedings of the 32st AAAI Conference on Artificial
Intelligence. AAAI Press; 2018:6581-6588.'
apa: 'Haller, S., Swoboda, P., & Savchynskyy, B. (2018). Exact MAP-inference
by confining combinatorial search with LP relaxation. In Proceedings of the
32st AAAI Conference on Artificial Intelligence (pp. 6581–6588). New Orleans,
LU, United States: AAAI Press.'
chicago: Haller, Stefan, Paul Swoboda, and Bogdan Savchynskyy. “Exact MAP-Inference
by Confining Combinatorial Search with LP Relaxation.” In Proceedings of the
32st AAAI Conference on Artificial Intelligence, 6581–88. AAAI Press, 2018.
ieee: S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining
combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference
on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588.
ista: 'Haller S, Swoboda P, Savchynskyy B. 2018. Exact MAP-inference by confining
combinatorial search with LP relaxation. Proceedings of the 32st AAAI Conference
on Artificial Intelligence. AAAI: Conference on Artificial Intelligence, 6581–6588.'
mla: Haller, Stefan, et al. “Exact MAP-Inference by Confining Combinatorial Search
with LP Relaxation.” Proceedings of the 32st AAAI Conference on Artificial
Intelligence, AAAI Press, 2018, pp. 6581–88.
short: S. Haller, P. Swoboda, B. Savchynskyy, in:, Proceedings of the 32st AAAI
Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–6588.
conference:
end_date: 2018-02-07
location: New Orleans, LU, United States
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2018-02-02
date_created: 2019-02-13T13:32:48Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-09-19T14:26:52Z
day: '01'
department:
- _id: VlKo
external_id:
arxiv:
- '2004.06370'
isi:
- '000485488906082'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.06370
month: '02'
oa: 1
oa_version: Preprint
page: 6581-6588
publication: Proceedings of the 32st AAAI Conference on Artificial Intelligence
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Exact MAP-inference by confining combinatorial search with LP relaxation
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '641'
abstract:
- lang: eng
text: 'We introduce two novel methods for learning parameters of graphical models
for image labelling. The following two tasks underline both methods: (i) perturb
model parameters based on given features and ground truth labelings, so as to
exactly reproduce these labelings as optima of the local polytope relaxation of
the labelling problem; (ii) train a predictor for the perturbed model parameters
so that improved model parameters can be applied to the labelling of novel data.
Our first method implements task (i) by inverse linear programming and task (ii)
using a regressor e.g. a Gaussian process. Our second approach simultaneously
solves tasks (i) and (ii) in a joint manner, while being restricted to linearly
parameterised predictors. Experiments demonstrate the merits of both approaches.'
alternative_title:
- LNCS
author:
- first_name: Vera
full_name: Trajkovska, Vera
last_name: Trajkovska
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Freddie
full_name: Åström, Freddie
last_name: Åström
- first_name: Stefanie
full_name: Petra, Stefanie
last_name: Petra
citation:
ama: 'Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning
by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol
10302. Springer; 2017:323-334. doi:10.1007/978-3-319-58771-4_26'
apa: 'Trajkovska, V., Swoboda, P., Åström, F., & Petra, S. (2017). Graphical
model parameter learning by inverse linear programming. In F. Lauze, Y. Dong,
& A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM:
Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer.
https://doi.org/10.1007/978-3-319-58771-4_26'
chicago: Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical
Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze,
Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_26.
ieee: 'V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter
learning by inverse linear programming,” presented at the SSVM: Scale Space and
Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp.
323–334.'
ista: 'Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter
learning by inverse linear programming. SSVM: Scale Space and Variational Methods
in Computer Vision, LNCS, vol. 10302, 323–334.'
mla: Trajkovska, Vera, et al. Graphical Model Parameter Learning by Inverse Linear
Programming. Edited by François Lauze et al., vol. 10302, Springer, 2017,
pp. 323–34, doi:10.1007/978-3-319-58771-4_26.
short: V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A.
Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334.
conference:
end_date: 2017-06-08
location: Kolding, Denmark
name: 'SSVM: Scale Space and Variational Methods in Computer Vision'
start_date: 2017-06-04
date_created: 2018-12-11T11:47:39Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2021-01-12T08:07:23Z
day: '01'
department:
- _id: VlKo
doi: 10.1007/978-3-319-58771-4_26
editor:
- first_name: François
full_name: Lauze, François
last_name: Lauze
- first_name: Yiqiu
full_name: Dong, Yiqiu
last_name: Dong
- first_name: Anders
full_name: Bjorholm Dahl, Anders
last_name: Bjorholm Dahl
intvolume: ' 10302'
language:
- iso: eng
month: '01'
oa_version: None
page: 323 - 334
publication_identifier:
isbn:
- 978-331958770-7
publication_status: published
publisher: Springer
publist_id: '7147'
quality_controlled: '1'
scopus_import: 1
status: public
title: Graphical model parameter learning by inverse linear programming
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 10302
year: '2017'
...
---
_id: '646'
abstract:
- lang: eng
text: We present a novel convex relaxation and a corresponding inference algorithm
for the non-binary discrete tomography problem, that is, reconstructing discrete-valued
images from few linear measurements. In contrast to state of the art approaches
that split the problem into a continuous reconstruction problem for the linear
measurement constraints and a discrete labeling problem to enforce discrete-valued
reconstructions, we propose a joint formulation that addresses both problems simultaneously,
resulting in a tighter convex relaxation. For this purpose a constrained graphical
model is set up and evaluated using a novel relaxation optimized by dual decomposition.
We evaluate our approach experimentally and show superior solutions both mathematically
(tighter relaxation) and experimentally in comparison to previously proposed relaxations.
alternative_title:
- LNCS
author:
- first_name: Jan
full_name: Kuske, Jan
last_name: Kuske
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Stefanie
full_name: Petra, Stefanie
last_name: Petra
citation:
ama: 'Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete
tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246.
doi:10.1007/978-3-319-58771-4_19'
apa: 'Kuske, J., Swoboda, P., & Petra, S. (2017). A novel convex relaxation
for non binary discrete tomography. In F. Lauze, Y. Dong, & A. Bjorholm Dahl
(Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational
Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_19'
chicago: Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation
for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and
Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_19.
ieee: 'J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary
discrete tomography,” presented at the SSVM: Scale Space and Variational Methods
in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.'
ista: 'Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary
discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision,
LNCS, vol. 10302, 235–246.'
mla: Kuske, Jan, et al. A Novel Convex Relaxation for Non Binary Discrete Tomography.
Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:10.1007/978-3-319-58771-4_19.
short: J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl
(Eds.), Springer, 2017, pp. 235–246.
conference:
end_date: 2017-06-08
location: Kolding, Denmark
name: 'SSVM: Scale Space and Variational Methods in Computer Vision'
start_date: 2017-06-04
date_created: 2018-12-11T11:47:41Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2021-01-12T08:07:34Z
day: '01'
department:
- _id: VlKo
doi: 10.1007/978-3-319-58771-4_19
ec_funded: 1
editor:
- first_name: François
full_name: Lauze, François
last_name: Lauze
- first_name: Yiqiu
full_name: Dong, Yiqiu
last_name: Dong
- first_name: Anders
full_name: Bjorholm Dahl, Anders
last_name: Bjorholm Dahl
intvolume: ' 10302'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.03769
month: '06'
oa: 1
oa_version: Submitted Version
page: 235 - 246
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_identifier:
isbn:
- 978-331958770-7
publication_status: published
publisher: Springer
publist_id: '7132'
quality_controlled: '1'
scopus_import: 1
status: public
title: A novel convex relaxation for non binary discrete tomography
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 10302
year: '2017'
...
---
_id: '915'
abstract:
- lang: eng
text: We propose a dual decomposition and linear program relaxation of the NP-hard
minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut
polytope, it is amenable to efficient optimization by message passing. Like other
polyhedral relaxations, it can be tightened efficiently by cutting planes. We
define an algorithm that alternates between message passing and efficient separation
of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art
algorithms based on linear programming, including algorithms written in the framework
of leading commercial software, as we show in experiments with large instances
of the problem from applications in computer vision, biomedical image analysis
and data mining.
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Bjoern
full_name: Andres, Bjoern
last_name: Andres
citation:
ama: 'Swoboda P, Andres B. A message passing algorithm for the minimum cost multicut
problem. In: Vol 2017. IEEE; 2017:4990-4999. doi:10.1109/CVPR.2017.530'
apa: 'Swoboda, P., & Andres, B. (2017). A message passing algorithm for the
minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR:
Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.530'
chicago: Swoboda, Paul, and Bjoern Andres. “A Message Passing Algorithm for the
Minimum Cost Multicut Problem,” 2017:4990–99. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.530.
ieee: 'P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost
multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition,
Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.'
ista: 'Swoboda P, Andres B. 2017. A message passing algorithm for the minimum cost
multicut problem. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4990–4999.'
mla: Swoboda, Paul, and Bjoern Andres. A Message Passing Algorithm for the Minimum
Cost Multicut Problem. Vol. 2017, IEEE, 2017, pp. 4990–99, doi:10.1109/CVPR.2017.530.
short: P. Swoboda, B. Andres, in:, IEEE, 2017, pp. 4990–4999.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:11Z
date_published: 2017-07-01T00:00:00Z
date_updated: 2023-09-26T15:43:27Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1109/CVPR.2017.530
ec_funded: 1
external_id:
isi:
- '000418371405009'
file:
- access_level: open_access
checksum: 7e51dacefa693574581a32da3eff63dc
content_type: application/pdf
creator: dernst
date_created: 2019-01-18T12:52:46Z
date_updated: 2020-07-14T12:48:15Z
file_id: '5849'
file_name: Swoboda_A_Message_Passing_CVPR_2017_paper.pdf
file_size: 883264
relation: main_file
file_date_updated: 2020-07-14T12:48:15Z
has_accepted_license: '1'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 4990-4999
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6526'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A message passing algorithm for the minimum cost multicut problem
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '916'
abstract:
- lang: eng
text: We study the quadratic assignment problem, in computer vision also known as
graph matching. Two leading solvers for this problem optimize the Lagrange decomposition
duals with sub-gradient and dual ascent (also known as message passing) updates.
We explore this direction further and propose several additional Lagrangean relaxations
of the graph matching problem along with corresponding algorithms, which are all
based on a common dual ascent framework. Our extensive empirical evaluation gives
several theoretical insights and suggests a new state-of-the-art anytime solver
for the considered problem. Our improvement over state-of-the-art is particularly
visible on a new dataset with large-scale sparse problem instances containing
more than 500 graph nodes each.
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Carsten
full_name: Rother, Carsten
last_name: Rother
- first_name: Carsten
full_name: Abu Alhaija, Carsten
last_name: Abu Alhaija
- first_name: Dagmar
full_name: Kainmueller, Dagmar
last_name: Kainmueller
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. A study
of lagrangean decompositions and dual ascent solvers for graph matching. In: Vol
2017. IEEE; 2017:7062-7071. doi:10.1109/CVPR.2017.747'
apa: 'Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., & Savchynskyy,
B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph
matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and
Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.747'
chicago: Swoboda, Paul, Carsten Rother, Carsten Abu Alhaija, Dagmar Kainmueller,
and Bogdan Savchynskyy. “A Study of Lagrangean Decompositions and Dual Ascent
Solvers for Graph Matching,” 2017:7062–71. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.747.
ieee: 'P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy,
“A study of lagrangean decompositions and dual ascent solvers for graph matching,”
presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
United States, 2017, vol. 2017, pp. 7062–7071.'
ista: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. 2017. A
study of lagrangean decompositions and dual ascent solvers for graph matching.
CVPR: Computer Vision and Pattern Recognition vol. 2017, 7062–7071.'
mla: Swoboda, Paul, et al. A Study of Lagrangean Decompositions and Dual Ascent
Solvers for Graph Matching. Vol. 2017, IEEE, 2017, pp. 7062–71, doi:10.1109/CVPR.2017.747.
short: P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, B. Savchynskyy, in:,
IEEE, 2017, pp. 7062–7071.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:11Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2023-09-26T15:41:40Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1109/CVPR.2017.747
ec_funded: 1
external_id:
isi:
- '000418371407018'
file:
- access_level: open_access
checksum: e38a2740daad1ea178465843b5072906
content_type: application/pdf
creator: dernst
date_created: 2019-01-18T12:49:38Z
date_updated: 2020-07-14T12:48:15Z
file_id: '5848'
file_name: 2017_CVPR_Swoboda2.pdf
file_size: 944332
relation: main_file
file_date_updated: 2020-07-14T12:48:15Z
has_accepted_license: '1'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Submitted Version
page: 7062-7071
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6525'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A study of lagrangean decompositions and dual ascent solvers for graph matching
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '917'
abstract:
- lang: eng
text: We propose a general dual ascent framework for Lagrangean decomposition
of combinatorial problems. Although methods of this type have shown their efficiency
for a number of problems, so far there was no general algorithm applicable to
multiple problem types. In this work, we propose such a general algorithm. It
depends on several parameters, which can be used to optimize its performance in
each particular setting. We demonstrate efficacy of our method on graph matching
and multicut problems, where it outperforms state-of-the-art solvers including
those based on subgradient optimization and off-the-shelf linear programming solvers.
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Jan
full_name: Kuske, Jan
last_name: Kuske
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: 'Swoboda P, Kuske J, Savchynskyy B. A dual ascent framework for Lagrangean
decomposition of combinatorial problems. In: Vol 2017. IEEE; 2017:4950-4960. doi:10.1109/CVPR.2017.526'
apa: 'Swoboda, P., Kuske, J., & Savchynskyy, B. (2017). A dual ascent framework
for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960).
Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
United States: IEEE. https://doi.org/10.1109/CVPR.2017.526'
chicago: Swoboda, Paul, Jan Kuske, and Bogdan Savchynskyy. “A Dual Ascent Framework
for Lagrangean Decomposition of Combinatorial Problems,” 2017:4950–60. IEEE, 2017.
https://doi.org/10.1109/CVPR.2017.526.
ieee: 'P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean
decomposition of combinatorial problems,” presented at the CVPR: Computer Vision
and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.'
ista: 'Swoboda P, Kuske J, Savchynskyy B. 2017. A dual ascent framework for Lagrangean
decomposition of combinatorial problems. CVPR: Computer Vision and Pattern Recognition
vol. 2017, 4950–4960.'
mla: Swoboda, Paul, et al. A Dual Ascent Framework for Lagrangean Decomposition
of Combinatorial Problems. Vol. 2017, IEEE, 2017, pp. 4950–60, doi:10.1109/CVPR.2017.526.
short: P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:11Z
date_published: 2017-07-01T00:00:00Z
date_updated: 2023-09-26T15:41:11Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1109/CVPR.2017.526
ec_funded: 1
external_id:
isi:
- '000418371405005'
file:
- access_level: open_access
checksum: 72fd291046bd8e5717961bd68f6b6f03
content_type: application/pdf
creator: dernst
date_created: 2019-01-18T12:45:55Z
date_updated: 2020-07-14T12:48:15Z
file_id: '5847'
file_name: 2017_CVPR_Swoboda.pdf
file_size: 898652
relation: main_file
file_date_updated: 2020-07-14T12:48:15Z
has_accepted_license: '1'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 4950-4960
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6524'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A dual ascent framework for Lagrangean decomposition of combinatorial problems
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '5557'
abstract:
- lang: eng
text: "Small synthetic discrete tomography problems.\r\nSizes are 32x32, 64z64 and
256x256.\r\nProjection angles are 2, 4, and 6.\r\nNumber of labels are 3 and 5."
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
citation:
ama: Swoboda P. Synthetic discrete tomography problems. 2016. doi:10.15479/AT:ISTA:46
apa: Swoboda, P. (2016). Synthetic discrete tomography problems. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:46
chicago: Swoboda, Paul. “Synthetic Discrete Tomography Problems.” Institute of Science
and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:46.
ieee: P. Swoboda, “Synthetic discrete tomography problems.” Institute of Science
and Technology Austria, 2016.
ista: Swoboda P. 2016. Synthetic discrete tomography problems, Institute of Science
and Technology Austria, 10.15479/AT:ISTA:46.
mla: Swoboda, Paul. Synthetic Discrete Tomography Problems. Institute of
Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:46.
short: P. Swoboda, (2016).
contributor:
- contributor_type: data_collector
first_name: Jan
last_name: Kuske
datarep_id: '46'
date_created: 2018-12-12T12:31:31Z
date_published: 2016-09-20T00:00:00Z
date_updated: 2024-02-21T13:50:21Z
day: '20'
ddc:
- '006'
department:
- _id: VlKo
doi: 10.15479/AT:ISTA:46
file:
- access_level: open_access
checksum: aa5a16a0dc888da7186fb8fc45e88439
content_type: application/zip
creator: system
date_created: 2018-12-12T13:05:19Z
date_updated: 2020-07-14T12:47:02Z
file_id: '5645'
file_name: IST-2016-46-v1+1_discrete_tomography_synthetic.zip
file_size: 36058401
relation: main_file
file_date_updated: 2020-07-14T12:47:02Z
has_accepted_license: '1'
keyword:
- discrete tomography
month: '09'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
status: public
title: Synthetic discrete tomography problems
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2016'
...