Paper Info
Title
A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them
Abstract
We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the l(p) norm, 1 <= p <= infinity. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC). The norm-based description of GGC provides more elegant and mathematically better recognized framework of our earlier results from [18, 19]. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper [22] (min-cut/max-flow graph cut, random walker, shortest path/geodesic, Voronoi diagram, power watershed/shortest path forest), which optimize, via l(p) norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used l(p) energy function. Actually, the comparison of the GGC representable algorithms with that encompassed in the framework described in [22] constitutes the main contribution of this work.
Year
DOI
Venue
2012
10.1117/12.911810
Proceedings of SPIE
Keywords
Field
DocType
algorithms,image segmentation
Cut,Shortest path problem,Segmentation,Algorithm,Image segmentation,Voronoi diagram,Random walker algorithm,Norm (mathematics),Geodesic,Physics
Conference
Volume
ISSN
Citations 
8314
0277-786X
5
PageRank 
References 
Authors
0.44
25
4
Name
Order
Citations
PageRank
Krzysztof Ciesielski129629.71
Jayaram K. Udupa22481322.29
Alexandre X. Falcão31877132.30
Paulo A. V. Vechiatto Miranda431326.26