A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation
This work addresses efficiency bottlenecks in image segmentation algorithms for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of accelerating parametric maximum flow computations in image segmentation by introducing a parallel framework based on supergraphs, which enables real-time implementations on multi-core or GPU architectures.
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.