DocumentCode :
1139261
Title :
Image Segmentation with a Unified Graphical Model
Author :
Zhang, Lei ; Ji, Qiang
Author_Institution :
Rensselaer Polytech. Inst., Troy, NY, USA
Volume :
32
Issue :
8
fYear :
2010
Firstpage :
1406
Lastpage :
1425
Abstract :
We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specifically, we first propose to employ Conditional Random Field (CRF) to model the spatial relationships among image superpixel regions and their measurements. We then introduce a multilayer Bayesian Network (BN) to model the causal dependencies that naturally exist among different image entities, including image regions, edges, and vertices. The CRF model and the BN model are then systematically and seamlessly combined through the theories of Factor Graph to form a unified probabilistic graphical model that captures the complex relationships among different image entities. Using the unified graphical model, image segmentation can be performed through a principled probabilistic inference. Experimental results on the Weizmann horse data set, on the VOC2006 cow data set, and on the MSRC2 multiclass data set demonstrate that our approach achieves favorable results compared to state-of-the-art approaches as well as those that use either the BN model or CRF model alone.
Keywords :
belief networks; graph theory; image resolution; image segmentation; random processes; MSRC2 multiclass data set; VOC2006 cow data set; Weizmann horse data set; causal dependencies; causal relationships; conditional random field; factor graph; image entities; image segmentation; image superpixel regions; multilayer Bayesian network; noncausal relationships; principled probabilistic inference; random variables; spatial relationships; unified probabilistic graphical model; Bayesian Network; Conditional Random Field; Image segmentation; factor graph.; probabilistic graphical model;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2009.145
Filename :
5166449
Link To Document :
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