Paper Info
Title
Nonlinear Multiscale Graph Theory based Segmentation of Color Images
Abstract
In this paper the issue of image segmentation within the framework of nonlinear multiscale watersheds in combination with graph theory based techniques is addressed. First, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. In the subsequent step the obtained graph is partitioned using recursive graph cuts in a coarse to fine manner. In this way, we are able to combine scale and feature measures in a flexible way: the feature-set that is used to measure the dissimilarities may change as we progress in scale. We employ the Earth Mover's Distance on a featureset that combines color, scale and contrast features to measure the dissimilarity between the nodes in the graph. Experimental results demonstrate the efficiency of the proposed method for natural scene images.
Year
DOI
Venue
2006
10.1109/ICPR.2006.866
ICPR
Keywords
Field
DocType
graph theory,image colour analysis,image segmentation,nonlinear multiscale graph theory color image segmentation,nonlinear multiscale watersheds,recursive graph cuts
Cut,Graph theory,Computer vision,Graph,Nonlinear system,Pattern recognition,Computer science,Segmentation,Image segmentation,Artificial intelligence,Recursion
Conference
Volume
ISSN
ISBN
2
1051-4651
0-7695-2521-0
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Iris Vanhamel11009.96
H. Sahli2404.16
I. Pratikakis380936.03