ROMJIST Volume 29, No. 2, 2026, pp. 115-127, DOI: 10.59277/ROMJIST.2026.2.02
Vlad OLARU, Mihai FLOREA, Cristian SMINCHISESCU A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation
ABSTRACT: This paper presents a framework that supports the implementation of parallel solutions for the parametric maximum flow computational models widely 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), which 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.KEYWORDS: Computer vision; image segmentation; parametric maximum flows in graphs.Read full text (pdf)