Quiet: Faster belief propagation for images and related applications

Yasuhiro Fujiwara, Dennis Shasha

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

Abstract

Belief propagation over Markov random fields has been successfully used in many AI applications since it yields accurate inference results by iteratively updating messages between nodes. However, its high computation costs are a barrier to practical use. This paper presents an efficient approach to belief propagation. Our approach, Quiet, dynamically detects converged messages to skip unnecessary updates in each iteration while it theoretically guarantees to output the same results as the standard approach used to implement belief propagation. Experiments show that our approach is significantly faster than existing approaches without sacrificing inference quality.

Original languageEnglish (US)
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3497-3503
Number of pages7
ISBN (Electronic)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Fujiwara, Y., & Shasha, D. (2015). Quiet: Faster belief propagation for images and related applications. In M. Wooldridge, & Q. Yang (Eds.), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (pp. 3497-3503). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2015-January). International Joint Conferences on Artificial Intelligence.