DocumentCode :
594795
Title :
Document image matching using probabilistic graphical models
Author :
Li Liu ; Yue Lu ; Suen, Ching
Author_Institution :
Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
637
Lastpage :
640
Abstract :
A document image matching approach making use of probabilistic graphical models is proposed. The document image is first represented by a tree with the nodes in the tree corresponding to the regions in the image and the edges indicating the parent-child relationships between them, transforming the problem to tree matching. A graphical model, i.e. pairwise Markov Random Field is defined on the tree, in which sense the nodes are considered as random variables and the edges encode the relations among these variables in the probability domain. The tree matching problem is then formulated as Maximum a Posterior (MAP) inference over the graphical model and solved by belief propagation. Since the underlying graphical model is tree-structured, the exact inference can be obtained. With properly defined potential functions in the joint probability represented by the graphical model, the disparity in tree representations caused by different image capturing conditions can be tolerated as demonstrated in the encouraging experimental results.
Keywords :
Markov processes; belief maintenance; document image processing; image matching; inference mechanisms; MAP; Markov random field; belief propagation; document image matching approach; graphical model; image capturing conditions; joint probability; maximum a posterior inference; parent-child relationships; probabilistic graphical models; tree matching; Graphical models; Image edge detection; Image matching; Joints; Probabilistic logic; Random variables; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460215
Link To Document :
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