A Scalable Graph-Cut Algorithm for N-D Grids

Andrew Delong and Yuri Boykov

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, June 2008.


Global optimisation via s-t graph cuts is widely used in computer vision and graphics. To obtain high-resolution output, graph cut methods must construct massive N-D grid-graphs containing billions of vertices. We show that when these graphs do not fit into physical memory, current max-flow/min-cut algorithms—the workhorse of graph cut methods—are totally impractical. Others have resorted to banded or hierarchical approximation methods that get trapped in local minima, which loses the main benefit of global optimisation.

We enhance the push-relabel algorithm for maximum flow [Goldberg-Tarjan'88] with two practical contributions. First, true global minima can now be computed on immense grid-like graphs too large for physical memory. These graphs are ubiquitous in computer vision, medical imaging and graphics. Second, for commodity multi-core platforms our algorithm attains near-linear speedup with respect to number of processors. To achieve these goals, we generalised the standard relabeling operations associated with push-relabel.

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