Fast approximate energy minimization via graph cuts
Boykov, Y.; Veksler, O.; Zabih, R.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 23, Issue 11, Nov 2001 Page(s):1222 - 1239
Digital Object Identifier 10.1109/34.969114
Summary:Many tasks in computer vision involve assigning a label (such as
disparity) to every pixel. A common constraint is that the labels should
vary smoothly almost everywhere while preserving sharp discontinuities
that may exist, e.g., at object boundaries. These tasks are naturally
stated in terms of energy minimization. The authors consider a wide
class of energies with various smoothness constraints. Global
minimization of these energy functions is NP-hard even in the simplest
discontinuity-preserving case. Therefore, our focus is on efficient
approximation algorithms. We present two algorithms based on graph cuts
that efficiently find a local minimum with respect to two types of large
moves, namely expansion moves and swap moves. These moves can
simultaneously change the labels of arbitrarily large sets of pixels. In
contrast, many standard algorithms (including simulated annealing) use
small moves where only one pixel changes its label at a time. Our
expansion algorithm finds a labeling within a known factor of the global
minimum, while our swap algorithm handles more general energy functions.
Both of these algorithms allow important cases of discontinuity
preserving energies. We experimentally demonstrate the effectiveness of
our approach for image restoration, stereo and motion. On real data with
ground truth, we achieve 98 percent accuracy
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