Triadic split-merge sampler
| Autor: | Hai Xiang Lin, H. Jaap van der Herik, Anne C. van Rossum, Johan L.A. Dubbeldam |
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| Rok vydání: | 2018 |
| Předmět: |
Machine vision
Computer science Context (language use) Markov chain Monte Carlo 02 engineering and technology RANSAC 01 natural sciences Statistics::Computation Hough transform law.invention Bayesian statistics 010104 statistics & probability symbols.namesake Data point law 0202 electrical engineering electronic engineering information engineering symbols Statistical inference 020201 artificial intelligence & image processing 0101 mathematics Algorithm |
| Zdroj: | ICMV |
| DOI: | 10.1117/12.2309466 |
| Popis: | In machine vision typical heuristic methods to extract parameterized objects out of raw data points are the Hough transform and RANSAC. Bayesian models carry the promise to optimally extract such parameterized objects given a correct definition of the model and the type of noise at hand. A category of solvers for Bayesian models are Markov chain Monte Carlo methods. Naive implementations of MCMC methods suffer from slow convergence in machine vision due to the complexity of the parameter space. Towards this blocked Gibbs and split-merge samplers have been developed that assign multiple data points to clusters at once. In this paper we introduce a new split-merge sampler, the triadic split-merge sampler, that perform steps between two and three randomly chosen clusters. This has two advantages. First, it reduces the asymmetry between the split and merge steps. Second, it is able to propose a new cluster that is composed out of data points from two different clusters. Both advantages speed up convergence which we demonstrate on a line extraction problem. We show that the triadic split-merge sampler outperforms the conventional split-merge sampler. Although this new MCMC sampler is demonstrated in this machine vision context, its application extend to the very general domain of statistical inference. |
| Databáze: | OpenAIRE |
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