Grab Cut Interactive Foreground Extraction using Iterated Graph

Grab. Cut Interactive Foreground Extraction using Iterated Graph Cuts Carsten Rother Vladimir Kolmogorov Andrew Blake Microsoft Research Cambridge-UK

Photomontage Grab. Cut – Interactive Foreground Extraction 1

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Problem Fast & Accurate ? Grab. Cut – Interactive Foreground Extraction 2

What Grab. Cut does Magic Wand (198? ) Intelligent Scissors Mortensen and Barrett (1995) Grab. Cut User Input Result Regions Boundary Regions & Boundary Grab. Cut – Interactive Foreground Extraction 3

Framework Input: Image Output: Segmentation Parameters: Colour , Coherence Energy: Optimization: Grab. Cut – Interactive Foreground Extraction 4

Graph Cuts Boykov and Jolly (2001) Image Foreground (source) Min Cut Background (sink) Cut: separating source and sink; Energy: collection of edges Min Cut: Global minimal enegry in polynomial time Grab. Cut – Interactive Foreground Extraction 5

Iterated Graph Cut ? User Initialisation K-means for learning colour distributions Graph cuts to infer the segmentation Grab. Cut – Interactive Foreground Extraction 6

Iterated Graph Cuts Gu ar co ante nv ed er ge to 1 Result 2 3 4 Energy after each Iteration Grab. Cut – Interactive Foreground Extraction 7

Colour Model R Iterated graph cut Foregroun d& Backgroun d Backgroun G d R Foreground Backgroun d G Gaussian Mixture Model (typically 5 -8 components) Grab. Cut – Interactive Foreground Extraction 8

Coherence Model An object is a coherent set of pixels: Blake et al. (2004): Learn jointly Grab. Cut – Interactive Foreground Extraction 9

Moderately straightforward examples … Grab. Cut completes automatically Grab. Cut – Interactive Foreground Extraction 10

Difficult Examples Camouflage & Low Contrast Fine structure No telepathy Initial Rectangle Initial Result Grab. Cut – Interactive Foreground Extraction 11

Evaluation – Labelled Database Available online: http: //research. microsoft. com/vision/cambridge/segmentation/ Grab. Cut – Interactive Foreground Extraction 12

Comparison Boykov and Jolly (2001) Grab. Cut User Input Result Error Rate: 1. 87% 1. 81% 1. 32% 1. 25% 0. 72% Error Rate: 0. 72% Grab. Cut – Interactive Foreground Extraction 13

Summary Magic Wand (198? ) Intelligent Lazy. Snappin Graph Scissors g Cuts Mortensen and Li et al. Boykov Barrett (1995) and (2004) Grab. Cut – Interactive Foreground Extraction Grab. Cut Rother et al. (2004) 22