Image Segmentation with a Bounding Box Priorvision.stanford.edu/teaching/cs231b_spring1415/slides/lempitsky_presentation.pdfPresentation Overview Segmentation problem description Background

Image Segmentation with a Bounding Box Prior

Victor Lempitsky, Pushmeet Kohli, Carsten Rother, Toby SharpMicrosoft Research Cambridge

Dylan Rhodes and Jasper Lin

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Segmentation Problem

How does one separate the foreground from the background with minimal user input?

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Bounding Box

● Allows the algorithm to focus on subimage● Desired segmentation is close to sides of bounding

box

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Bounding Box

● Allows the algorithm to focus on subimage● Desired segmentation is close to sides of bounding

box

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Basic Formulation

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Basic Formulation

B is the set of pixels within the bounding box

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Basic Formulation

E is the set of adjacent pixels within the bounding box

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Basic Formulation

p and q are pixel indices

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Basic Formulation

x_p can take a label of 1 for foreground or 0 for background

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Basic Formulation

Unary potentials encode preference for foreground or background

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Basic Formulation

Pairwise potentials enforce smoothness of the solution

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Related Work

● Nowozin and Lampert derived framework for segmentation under connectivity constraint

● Relax NP-hard integer problem and solve resulting LP

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Nowozin and Lampert

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Nowozin and Lampert

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Nowozin and Lampert

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

19

Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Why tightness?

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Tightness Definition

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Corollary

A shape x is strongly tight if and only if its intersection with the middle box has a connected component touching all four sides of the middle box

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Energy Minimization Problem

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Energy Minimization Problem

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Energy Minimization Problem

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Convex Continuous Relaxation

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Convex Continuous Relaxation

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Continuous Optimization

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Continuous Optimization

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Additional Approximation

Intuition: Solve LP with a subset Γ' of the constraints in 3c activated

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Calculating Γ'

1. Begin with Γ' = ∅2. Solve the LP3. Pick a group of crossing paths from Γ \ Γ' which are

violated by more than a small tolerance 4. Add these paths to Γ'5. Repeat steps 2 through 4 until all paths in Γ are

satisfied within the tolerance

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Final Form

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Pinpointing Algorithm

Normally, output of LP is rounded to integer solution

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Pinpointing Algorithm

● Pinpoint set Π contains pixels hard-assigned to foreground

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Pinpoint Algorithm

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Pinpoint Algorithm

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Challenges

● Existing methods perform energy-driven shrinking over bounding box○ No guarantees optimization won’t shrink excessively

○ Stuck at poor local minima

○ Discretization of approximate solution is noisy

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Paper’s Contributions

● Common methods initialize foreground region and perform energy-driven shrinking○ No guarantees optimization won’t shrink excessively

Solution: new tightness prior○ Stuck at poor local minima

○ Discretization of approximate solution is noisy

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Paper’s Contributions

● Common methods initialize foreground region and perform energy-driven shrinking○ No guarantees optimization won’t shrink excessively

Solution: new tightness prior○ Stuck at poor local minima

Solution: new approximation strategies○ Discretization of approximate solution is noisy

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Paper’s Contributions

● Common methods initialize foreground region and perform energy-driven shrinking○ No guarantees optimization won’t shrink excessively

Solution: new tightness prior○ Stuck at poor local minima

Solution: new approximation strategy○ Discretization of approximate solution is noisy

Solution: new pinpointing algorithm

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Presentation Overview

● Segmentation problem description● Background and Previous Work● Problems and Proposed Solutions

○ Formalizing tightness○ Defining tractable optimization problem for

segmentation○ Discretizing continuous approximation of solution

● Experiments and Results

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Experiments

● Evaluated over 50 image GrabCut dataset○ Each image comes with bounding box

● Comparison with competing methods and initialization strategies

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GrabCut Dataset

● 50 natural images with bounding box annotations○ Includes background, outside strip, and foreground

bounding boxes

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GrabCut Dataset

● 50 natural images with bounding box annotations○ Includes background, outside strip, and foreground

bounding boxes

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Unary and Pairwise Terms

● Pairwise terms over 8-connected edge set

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Relative Performance

Error rate - mislabeled pixels inside bounding boxOptimum Rank - average rank of energy of final integer program solutions

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Relative Performance

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Iterative Process

● Compare the following algorithms on the segmentation task:○ GrabCut with standard graph cut minimization for

all segmentation steps○ GrabCut which enforces the tightness prior for all

segmentation steps● 5 iterations each

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Initialization Strategies

● Compare two methods for initializing foreground/background GMMs:○ InitThirds = same as Experiment 1 (outside strip +

best matches vs. poor matches) ○ InitFullBox which sets background GMM to

outside strip and foreground to whole interior of bounding box

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Iterative Process

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Effect of Margin Thickness

Error rates as function of margin thickness

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Strong vs. Weak Tightness

● Strong and weak tightness lead to similar error rates in general○ same error rate (3.7%) for best model (GrabCut-

Pinpoint/InitThirds)

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Iterative Process Comparisons

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Conclusions

● New bounding-box based prior for interactive image segmentation

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Conclusions

● New bounding-box based prior for interactive image segmentation

● Demonstrated segmentation tasks under this prior can be formulated as integer programs

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Conclusions

● New bounding-box based prior for interactive image segmentation

● Demonstrated segmentation tasks under this prior can be formulated as integer programs

● Developed new optimization approaches for approximate solution of these NP-hard problems○ Can be applied to other computer vision problems e.g.

other image segmentation or silhouettes in multi-view stereo

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Thank you!

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