  This paper presents a framework that supports the implementation of parallel
solutions for the widespread parametric maximum flow computational routines
used in image segmentation algorithms. The framework is based on supergraphs, a
special construction combining several image graphs into a larger one, and
works on various architectures (multi-core or GPU), either locally or remotely
in a cluster of computing nodes. The framework can also be used for performance
evaluation of parallel implementations of maximum flow algorithms. We present
the case study of a state-of-the-art image segmentation algorithm based on
graph cuts, Constrained Parametric Min-Cut (CPMC), that uses the parallel
framework to solve parametric maximum flow problems, based on a GPU
implementation of the well-known push-relabel algorithm. Our results indicate
that real-time implementations based on the proposed techniques are possible.
