DocumentCode :
2528169
Title :
Constrained compound Markov random Field Model for segmentation of color texture and scene images
Author :
Panda, Sucheta ; Nanda, P.K. ; Dey, Rahul
Author_Institution :
Dept. of Electr. Eng., Nat. Inst. of Technol., Rourkela
fYear :
2008
fDate :
19-21 Nov. 2008
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, we propose a constrained compound Markov random field model (MRF) to model color texture as well as scene images. Ohta (I1, I2, I3) color model is used as the color model for segmentation. Besides, intra plane model, the constrained model is modified to take care of inter-plane interaction as well. Hence, the model is called as double constrained compound MRF (DCCMRF) model. The problem is formulated as pixel labelling problem and the pixel labels are estimated using maximum a posteriori (MAP) criterion.The MAP estimates are obtained using hybrid algorithm. The DCCMRF model exhibited improved segmentation accuracy as compared to DCMRF, MRF, double MRF (DMRF), double Gauss MRF(DGMRF) and JSEG method. The proposed models have been successfully tested for two, four and five class problem.
Keywords :
Gaussian processes; Markov processes; image colour analysis; image segmentation; image texture; maximum likelihood estimation; MAP criterion; color texture segmentation; constrained compound Markov random field model; double Gauss MRF; double constrained compound MRF; interplane interaction; maximum a posteriori; scene images; Color; Degradation; Educational institutions; Gaussian processes; Hidden Markov models; Image segmentation; Labeling; Layout; Markov random fields; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2008 - 2008 IEEE Region 10 Conference
Conference_Location :
Hyderabad
Print_ISBN :
978-1-4244-2408-5
Electronic_ISBN :
978-1-4244-2409-2
Type :
conf
DOI :
10.1109/TENCON.2008.4766604
Filename :
4766604
Link To Document :
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