• DocumentCode
    160377
  • Title

    An introduction to Markov logic networks and application in video activity analysis

  • Author

    Guangchun Cheng ; Yiwen Wan ; Buckles, Bill P. ; Yan Huang

  • Author_Institution
    Comput. Sci. & Eng., Univ. of North Texas, Denton, TX, USA
  • fYear
    2014
  • fDate
    11-13 July 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    A Markov logic network (MLN) is a compact combination of logic representation of knowledge and probabilistic reasoning in Markov networks. We have seen its applications in different domains, however, few tried to explain or demonstrate the underneath reasons why MLN works. This paper gives an introduction to MLN using examples in the hope to help understand its elegance and booster the application. Application in video activity analysis was designed to demonstrate how MLN can be used in a specific domain, including feature extraction, logic predicate/formula design, and activity recognition through probabilistic reasoning.
  • Keywords
    Markov processes; image recognition; inference mechanisms; video signal processing; MLN; Markov logic networks; activity recognition; feature extraction; knowledge logic representation; logic formula design; logic predicate design; probabilistic reasoning; video activity analysis; Cognition; Equations; Grounding; Markov random fields; Mathematical model; Trajectory; Markov logic networks; action recognition; activity analysis; computer vision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on
  • Conference_Location
    Hefei
  • Print_ISBN
    978-1-4799-2695-4
  • Type

    conf

  • DOI
    10.1109/ICCCNT.2014.6963049
  • Filename
    6963049