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, p in [1,\infty].
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.
Moreover, it allows precise theoretical comparison of GGC representable algorithms with
the algorithms discussed in a recent paper on Power Watershed
(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.

**Conference Proceeding reprint.**

**Last modified March 25, 2012.**