Paper Info
Title
Fusion of Image Segmentations under Markov, Random Fields.
Abstract
In this study, a fast and efficient consensus segmentation method is proposed which fuses a set of baseline segmentation maps under an unsupervised Markov Random Fields (MRF) framework. The degree of consensus among the segmentation maps are estimated as the relative frequency of co-occurrences among the adjacent segments. Then, these relative frequencies are used to construct the energy function of an unsupervised MRF model. It is well-known that MRF framework is commonly used for formulating the spatial relationships among the super-pixels, under the Potts model. In this study, the Potts model is reorganized to represent the degree of consensus among the spatially adjacent segments (super-pixels).The proposed segmentation fusion method, called, Boosted-MRF, is tested in various experimental setups, and its performance is compared to the state of the art segmentation methods and satisfactory results are obtained.
Year
DOI
Venue
2014
10.1109/ICPR.2014.170
ICPR
Keywords
Field
DocType
Markov processes,image fusion,image segmentation,Boosted-MRF,Potts model,energy function,image segmentation fusion method,relative frequency,unsupervised MRF model,unsupervised Markov random field framework
Computer vision,Scale-space segmentation,Random field,Pattern recognition,Computer science,Segmentation,Markov chain,Segmentation-based object categorization,Image segmentation,Minification,Artificial intelligence,Potts model
Conference
ISSN
Citations 
PageRank 
1051-4651
0
0.34
References 
Authors
14
3
Name
Order
Citations
PageRank
Ozge Oztimur Karadag151.78
Fatos T. Yarman-Vural2153.51
Yarman Vural, F.T.3736.17