GrabCutGrabCut Interactive Foreground Extraction Interactive Foreground Extraction

using Iterated Graph Cutsusing Iterated Graph Cuts

Carsten RotherCarsten RotherVladimir Kolmogorov Vladimir Kolmogorov

Andrew BlakeAndrew Blake

Microsoft Research Cambridge-UKMicrosoft Research Cambridge-UK

GrabCutGrabCut Interactive Foreground Extraction Interactive Foreground Extraction

using Iterated Graph Cutsusing Iterated Graph Cuts

Carsten RotherCarsten RotherVladimir Kolmogorov Vladimir Kolmogorov

Andrew BlakeAndrew Blake

Microsoft Research Cambridge-UKMicrosoft Research Cambridge-UK

ProblemProblem ProblemProblem

GrabCut – Interactive Foreground Extraction 2

Fast & Accurate ?

Graph Cuts modelling in imagesGraph Cuts modelling in images Graph Cuts modelling in imagesGraph Cuts modelling in images

GrabCut – Interactive Foreground Extraction 5

ImageImage Min CutMin Cut

Cut: separating source and sink; Energy: collection of edges

Min Cut: Global minimal enegry in polynomial time

Foreground Foreground (source)(source)

BackgroundBackground(sink)(sink)

Graph Cuts for foreground extractionGraph Cuts for foreground extraction Graph Cuts for foreground extractionGraph Cuts for foreground extraction

GrabCut – Interactive Foreground Extraction 5

Min CutMin Cut

Foreground Foreground (source)(source)

BackgroundBackground(sink)(sink)

Assume we know foreground is white and background is black

GrabCut – Interactive Foreground Extraction 5

Min CutMin Cut

Foreground Foreground (source)(source)

BackgroundBackground(sink)(sink)

Assume we know foreground is white and background is black

Data term = (cost of assigning label)

Regularization = (cost of separating neighbors)

Graph Cuts for foreground extractionGraph Cuts for foreground extraction Graph Cuts for foreground extractionGraph Cuts for foreground extraction

GrabCut – Interactive Foreground Extraction 5

Min CutMin Cut

Foreground Foreground (source)(source)

BackgroundBackground(sink)(sink)

Assume we know foreground is white and background is black

Data term = whiteness(cost of assigning label)

Regularization =(cost of separating neighbors)

Graph Cuts for foreground extractionGraph Cuts for foreground extraction Graph Cuts for foreground extractionGraph Cuts for foreground extraction

GrabCut – Interactive Foreground Extraction 5

Min CutMin Cut

Foreground Foreground (source)(source)

BackgroundBackground(sink)(sink)

Assume we know foreground is white and background is black

Data term = whiteness(cost of assigning label)

Regularization = color match (cost of separating neighbors)

Graph Cuts for foreground extractionGraph Cuts for foreground extraction Graph Cuts for foreground extractionGraph Cuts for foreground extraction

We are all set now !We are all set now ! We are all set now !We are all set now !

User Initialisation

Learn foreground color model

Graph cuts to infer the

foreground

?

GrabCut – Interactive Foreground Extraction 6

Iterated Graph CutsIterated Graph Cuts Iterated Graph CutsIterated Graph Cuts

User Initialisation

Learn foreground color model

Graph cuts to infer the

foreground

?

GrabCut – Interactive Foreground Extraction 6

1 2 3 4

Iterated Graph CutsIterated Graph Cuts Iterated Graph CutsIterated Graph Cuts

GrabCut – Interactive Foreground Extraction 7

Energy after each IterationResult

Guaranteed to

converge

Moderately straightforward Moderately straightforward

examples examples

Moderately straightforward Moderately straightforward

examples examples

… GrabCut completes automatically

GrabCut – Interactive Foreground Extraction 10

Difficult ExamplesDifficult Examples Difficult ExamplesDifficult Examples

Camouflage &

Low ContrastNo telepathyFine structure

Initial Rectangle

InitialResult

GrabCut – Interactive Foreground Extraction 11