GrabCut Interactive Image (and Stereo) Segmentation Joon Jae Lee Keimyung University Welcome. I will present Grabcut – an Interactive tool for foreground extraction.

Characteristics Improved from graph cut – Use Gaussian Mixture Model (GMM) for clustering in color space – Iterative optimization User interaction greatly reduced compared with other methods (Magic Wand, Intelligent Scissors)

Photomontage GrabCut – Interactive Foreground Extraction 1 Here are some images from my last hiking trip in England. And would it be great if we could just do the following. Drag rectangle. Create a nice photomontage with an extreamly simple user interface. GrabCut – Interactive Foreground Extraction 1

Problem Fast & Accurate ? State the problem. Image object to extract and simple tool which gives high quality results with an alpha matte and even works for the case of camoflage. GrabCut – Interactive Foreground Extraction 3

Intelligent Scissors Mortensen and Barrett (1995) What GrabCut does Magic Wand (198?) Intelligent Scissors Mortensen and Barrett (1995) GrabCut User Input Result Basic idea is to combine the information which was used in the 2 well known tools: MW and IS. MS select a region of similar colour according to your input. Fat pen and the boundary snapps to high contrast edges. Grabcut combines it. And this allows us to simplify the user interface considerably by … Regions Boundary Regions & Boundary GrabCut – Interactive Foreground Extraction 4

Gaussian Mixture Model The probability of a pixel vector is represented as:

Gaussian Mixture Model Two sets of GMM, one for background, one for unknown region

Algorithm For each pixel assign a value α α = 0 for background and α = 1 for foreground Use graph cut to minimize a Gibbs energy: α: background / foreground label θ: GMM parameters k: GMM labeling z: pixel value

Iterative minimization Initialize background pixel to α = 0 and unknown region (draw box) to α = 1. Initialize two sets of GMMs. 1. Assign GMM labels to each pixel. (Which set of GMM is determined by α) 2. Graph cut minimize (α optimized, GMM label changed to corresponding set of GMM) 3. Update GMM parameters 4. Repeat from step 1 until convergence

Graph Cuts - Boykov and Jolly (2001) Foreground (source) Image Min Cut Cut: separating source and sink; Energy: collection of edges Min Cut: Global minimal enegry in polynomial time Optimization engine we use Graph Cuts. By know everybody should know what graph cut is. IN case you missed it here is a very brief introduction. To image. 3D view. First task to construct a graph. All pixels on a certain scanline. Just a few of them. Next step introduce artificial nodes. fgd and background. Edges – contrast. Boundary is quite likely between black and white. Min Cut - Minimum edge strength. Background (sink) GrabCut – Interactive Foreground Extraction 7

Guaranteed to converge Iterated Graph Cuts Guaranteed to converge Iterations are not shown to the user; Converges: proof in the paper. 1 2 3 4 Result Energy after each Iteration GrabCut – Interactive Foreground Extraction 9

Colour Model R R G G Gaussian Mixture Model (typically 5-8 components) Iterated graph cut Foreground & Background Foreground Color enbergy. Iterations have the effect of pulling them away; D is – log likelyhood of the GMM. Background G Background G Gaussian Mixture Model (typically 5-8 components) GrabCut – Interactive Foreground Extraction 10

Coherence Model An object is a coherent set of pixels: 25 Also coherent model. Strength of contrast – colour difference. Lambda importance of coherence model. 25 Error (%) over training set: How do we choose ? 25

Parameter Learning - Problems A Gaussian MRF is not a realistic texture model syntheticGMRF Gaussian? Real Image Gaussian! GrabCut – Interactive Foreground Extraction 13

Moderately simple examples Moderately straightforward examples- after the user input automnatically … GrabCut completes automatically GrabCut – Interactive Foreground Extraction 14

Difficult Examples Camouflage & Low Contrast Fine structure No telepathy Initial Rectangle Initial Result You might wonder when does it fail. 4 cases. Low contrats – an edge not good visible GrabCut – Interactive Foreground Extraction 15

Evaluation – Labelled Database Labeled data base 70 images . availoable online. Different scenarios – simple and ifficult shapes and colour. Available online: http://research.microsoft.com/vision/cambridge/segmentation/ GrabCut – Interactive Foreground Extraction 16

Comparison Boykov and Jolly (2001) GrabCut Error Rate: 0.72% User Input Error Rate: 0.72% Databsed used to compare to BJ. Re-implented their method. Simpler same error rate Result Error Rate: 0.72% Error Rate: 1.32% Error Rate: 1.87% Error Rate: 1.81% Error Rate: 1.25% GrabCut – Interactive Foreground Extraction 17

Trimap Boykov and Jolly Comparison Input Image Ground Truth Trimap Boykov and Jolly Error Rate: 1.36% Bimap GrabCut Error Rate: 2.13% Use our database. Small uncertainty area. Error rate - modestly increase User Interactions - considerable reduced GrabCut – Interactive Foreground Extraction 18

Results Parameter Learning GrabCut – Interactive Foreground Extraction 19

Comparison Magic Wand (198?) Finally – number of tools: certain scenarios. what does the user want to do for segmentation – Graph Cut – user input reduced and quality increases. Magic Wand (198?) Intelligent Scissors Mortensen and Barrett (1995) Graph Cuts Boykov and Jolly (2001) LazySnapping Li et al. (2004) GrabCut Rother et al. (2004) GrabCut – Interactive Foreground Extraction 20