GrabCut

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|date=March 2021}}

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|date=March 2021}}

There are several open source implementations available including OpenCV (as of version 2.1).{{citation needed|date=March 2021}}

See also

References

  • C. Rother, V. Kolmogorov, and A. Blake, [http://research.microsoft.com/apps/pubs/default.aspx?id=67890GrabCut: Interactive foreground extraction using iterated graph cuts], ACM Trans. Graph., vol. 23, pp. 309–314, 2004.

Category:Image segmentation