Slice Sampling Particle Belief Propagation

Oliver Muller, Michael Ying Yang, Bodo Rosenhahn

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Computer Vision
Number of pages8
ISBN (Electronic)978-1-4799-2840-8
Publication statusPublished - Dec 2013
EventIEEE International Conference on Computer Vision 2013 - Sydney Conference Centre, Sydney, Australia
Duration: 1 Dec 20138 Dec 2013


ConferenceIEEE International Conference on Computer Vision 2013
Abbreviated titleICCV 2013
Internet address

Fingerprint Dive into the research topics of 'Slice Sampling Particle Belief Propagation'. Together they form a unique fingerprint.

Cite this