GrabCut is an image segmentation method based on graph cuts.

Starting with a user-specified bounding box around the object to be segmented, the algorithm estimates the color distribution of the target object and that of the background using a Gaussian mixture model. This is used to construct a Markov random field over the pixel labels, with an energy function that prefers connected regions having the same label, and running a graph cut based optimization to infer their values. As this estimate is likely to be more accurate than the original, taken from the bounding box, this two-step procedure is repeated until convergence.[citation needed]

Estimates can be further corrected by the user by pointing out misclassified regions and rerunning the optimization. The method also corrects the results to preserve edges.[citation needed]

There are several open source implementations available including OpenCV (as of version 2.1).[citation needed]

See also

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References

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S2CID 1665580. Rother, Carsten; Kolmogorov, Vladimir; Blake, Andrew (2004). Grabcut: Interactive foreground extraction using iterated graph cuts (PDF). ACM