Preserving modes and messages via diverse particle selection

Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


In applications of graphical models arising in domains such as computer vision and signal pro-cessing, we often seek the most likely configurations of high-dimensional, continuous variables. We develop a particle-based max-product algorithm which maintains a diverse set of posterior mode hypotheses, and is robust to initialization. At each iteration, the set of hypotheses at each node is augmented via stochastic proposals, and then reduced via an efficient selection algorithm. The integer program underlying our optimization-based particle selection minimizes errors in subsequent max-product message updates. This objective automatically encourages diversity in the maintained hypotheses, without requiring tuning of application-specific distances among hypotheses. By avoiding the stochastic resampling steps underlying particle sum-product algorithms, we also avoid common degeneracies where particles collapse onto a single hypothesis. Our approach significantly out-performs previous particle-based algorithms in experiments focusing on the estimation of human pose from single images.

Original languageEnglish (US)
Title of host publication31st International Conference on Machine Learning, ICML 2014
PublisherInternational Machine Learning Society (IMLS)
Number of pages13
ISBN (Electronic)9781634393973
StatePublished - 2014
Externally publishedYes
Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
Duration: Jun 21 2014Jun 26 2014

Publication series

Name31st International Conference on Machine Learning, ICML 2014


Conference31st International Conference on Machine Learning, ICML 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software


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