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