DocumentCode :
703382
Title :
A new algorithm CGA for image labeling
Author :
Guo dong Guo ; Shan Yu ; Song de Ma
Author_Institution :
NLPR, Inst. of Autom., Beijing, China
fYear :
1998
fDate :
8-11 Sept. 1998
Firstpage :
1
Lastpage :
4
Abstract :
Many image analysis and computer vision problems can be formulated as a scene labeling problem in which each site is to be assigned a label from a discrete or continuous label set with contextual information. In this paper we present a new labeling algorithm based on the game theory. More precisely, we use Markov random fields to model images, and we design an n-person cooperative game which yields a deterministic optimization algorithm. Experimental results show that the algorithm is efficient and effective, exhibiting very fast convergence, and producing better result than the recently proposed non-cooperative game approach. We also compare this algorithm with other labeling algorithms on real world and synthetic images.
Keywords :
Markov processes; computer vision; deterministic algorithms; game theory; optimisation; CGA; Markov random field; computer vision problems; continuous label set; deterministic optimization algorithm; discrete label set; image labeling; n-person cooperative game theory; noncooperative game approach; scene labeling problem; Algorithm design and analysis; Bayes methods; Computer vision; Game theory; Games; Labeling; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO 1998), 9th European
Conference_Location :
Rhodes
Print_ISBN :
978-960-7620-06-4
Type :
conf
Filename :
7089853
Link To Document :
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