Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings |
| Untertitel | 2013 IEEE International Conference on Computer Vision, ICCV 2013 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1129-1136 |
| Seitenumfang | 8 |
| ISBN (Print) | 9781479928392 |
| Publikationsstatus | Veröffentlicht - 2013 |
| Veranstaltung | 2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australien Dauer: 1 Dez. 2013 → 8 Dez. 2013 |
Publikationsreihe
| Name | Proceedings of the IEEE International Conference on Computer Vision |
|---|
Abstract
Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings (MH) Markov chain Monte Carlo (MCMC) methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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Proceedings: 2013 IEEE International Conference on Computer Vision, ICCV 2013. Institute of Electrical and Electronics Engineers Inc., 2013. S. 1129-1136 6751250 (Proceedings of the IEEE International Conference on Computer Vision).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Slice sampling particle belief propagation
AU - Muller, Oliver
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
PY - 2013
Y1 - 2013
N2 - Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings (MH) Markov chain Monte Carlo (MCMC) methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.
AB - Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings (MH) Markov chain Monte Carlo (MCMC) methods which involves sampling from a proposal distribution. This proposal distribution has to be carefully designed depending on the particular model and input data to achieve fast convergence. We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm. The proposed approach shows superior convergence performance on an image denoising toy example. Our findings are validated on a challenging relational 2D feature tracking application.
KW - Feature Tracking
KW - MCMC
KW - Optimization
KW - Particle Belief Propagation
KW - Slice Sampling
UR - http://www.scopus.com/inward/record.url?scp=84898811282&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2013.144
DO - 10.1109/ICCV.2013.144
M3 - Conference contribution
AN - SCOPUS:84898811282
SN - 9781479928392
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1129
EP - 1136
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 14th IEEE International Conference on Computer Vision, ICCV 2013
Y2 - 1 December 2013 through 8 December 2013
ER -