  Rapid advances in image acquisition and storage technology underline the need
for algorithms that are capable of solving large scale image processing and
computer-vision problems. The minimum cut problem plays an important role in
processing many of these imaging problems such as, image and video
segmentation, stereo vision, multi-view reconstruction and surface fitting.
While several min-cut/max-flow algorithms can be found in the literature, their
performance in practice has been studied primarily outside the scope of
computer vision. We present here the results of a comprehensive computational
study, in terms of execution times and memory utilization, of four recently
published algorithms, which optimally solve the {\em s-t} cut and maximum flow
problems: (i) Goldberg's and Tarjan's {\em Push-Relabel}; (ii) Hochbaum's {\em
pseudoflow}; (iii) Boykov's and Kolmogorov's {\em augmenting paths}; and (iv)
Goldberg's {\em partial augment-relabel}. Our results demonstrate that the {\em
Hochbaum's pseudoflow} algorithm, is faster and utilizes less memory than the
other algorithms on all problem instances investigated.
