Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2013 IEEE International Conference on Computer Vision, ICCV 2013 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1129-1136 |
| Number of pages | 8 |
| ISBN (print) | 9781479928392 |
| Publication status | Published - 2013 |
| Event | 2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia Duration: 1 Dec 2013 → 8 Dec 2013 |
Publication series
| 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.
Keywords
- Feature Tracking, MCMC, Optimization, Particle Belief Propagation, Slice Sampling
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
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Proceedings: 2013 IEEE International Conference on Computer Vision, ICCV 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 1129-1136 6751250 (Proceedings of the IEEE International Conference on Computer Vision).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -