Slice Sampling Particle Belief Propagation

Oliver Muller, Michael Ying Yang, Bodo Rosenhahn

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

8 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


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