Paper Info
Title
Color image segmentation using an enhanced Gradient Network Method
Abstract
The objective of this paper is to evaluate a new combined approach intended for reliable color image segmentation, in particular images presenting color structures with strong but continuous color or luminosity changes, such as commonly found in outdoors scenes. The approach combines an enhanced version of the Gradient Network 2, with common region-growing approaches used as pre-segmentation steps. The GNM2 is an post-segmentation procedure based on graph analysis of global color and luminosity gradients in conjunction with a segmentation algorithm to produce a reliable segmentation result. The approach was automatically evaluated using a close/open world approach. Two different region-growing segmentation methods, CSC and Mumford and Shah with and without the GNM post-processing were compared against ground truth images using segmentation evaluation indices Rand and Bipartite Graph Matching. These results were also confronted with other well established segmentation methods (RHSEG, Watershed, EDISON, JSEG and Blobworld).
Year
DOI
Venue
2009
10.1016/j.patrec.2009.07.005
Pattern Recognition Letters
Keywords
DocType
Volume
region-growing,reliable segmentation result,segmentation method,color image segmentation,segmentation algorithm,outdoors scenes,continuous color,segmentation evaluation,gradient network method,enhanced gradient network method,color structure,new combined approach,reliable color image segmentation,global color,different region-growing segmentation method,bipartite graph,region growing,ground truth
Journal
30
Issue
ISSN
Citations 
15
Pattern Recognition Letters
9
PageRank 
References 
Authors
0.48
26
9
Name
Order
Citations
PageRank
A. V. Wangenheim1101.23
R. F. Bertoldi290.48
D. D. Abdala3111.40
A. Sobieranski490.48
L. Coser590.48
X. Jiang6131.70
M. M. Richter790.48
Lutz Priese824031.41
Frank Schmitt9264.29