Image Segmentation Using Graph Cuts Based on Maximum-Flow Neural Network

Abstract

Graph Cuts has became increasingly useful methods for the image segmentation. In Graph Cuts, given images are replaced by grid graphs, and the image segmentation process is performed using the minimum cut (min-cut) algorithm on the graphs. For Graph Cuts, the most typical min-cut algorithm is the B-K algorithm. While the B-K algorithm is very efficient, it is still far from real-time processing. In addition, the B-K algorithm gives only the single min-cut even if the graph has multiple-min-cuts. The conventional Graph Cuts has a possibility that a better minimum cut for an image segmentation is frequently overlooked. Therefore, it is important to apply a more effective min-cut algorithm to Graph Cuts. In this research, we propose a new image segmentation technique using Graph Cuts based on the maximum-flow neural network (MF-NN). The MF-NN is our proposed min-cut algorithm based on a nonlinear resistive circuit analysis. By applying the MF-NN to Graph Cuts instead of the B-K algorithm, image segmentation problems can be solved as the nonlinear resistive circuits analysis. In addition, the MF-NN has an unique feature that multiple-min-cuts can be find easily. That is, it can be expected that our proposed method can obtain more accurate results than the conventional Graph Cuts which generates only one min-cut. When the proposed circuit model is designed with the integrated circuit which can change graph structure and branch conductance, a novel image segmentation technique with real-time processing can be expected.