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
Link To Document :
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