discrete optimization lecture 5 – part 2 m. pawan kumar [email protected] slides available online

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Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar [email protected] Slides available online http://mpawankumar.info

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Page 1: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Discrete OptimizationLecture 5 – Part 2

M. Pawan Kumar

[email protected]

Slides available online http://mpawankumar.info

Page 2: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Interactive Binary Segmentation

Foreground histogram of RGB values FG

Background histogram of RGB values BG

‘1’ indicates foreground and ‘0’ indicates background

Page 3: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Interactive Binary Segmentation

More likely to be foreground than background

Page 4: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Interactive Binary Segmentation

More likely to be background than foreground

θa(0) proportional to -log(BG(da))

θa(1) proportional to -log(FG(da))

Page 5: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Interactive Binary Segmentation

More likely to belong to same label

Page 6: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Interactive Binary Segmentation

Less likely to belong to same label

θab(i,k) proportional to exp(-(da-db)2) if i ≠ k

θab(i,k) = 0 if i = k

Page 7: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Outline

• Minimum Cut Problem

• Submodular Energy Functions

Page 8: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Directed Graph

n1 n2

n3 n4

10

5

3 2

Important restriction

Positive arc lengths

D = (N, A)

Page 9: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Cut

n1 n2

n3 n4

10

5

3 2

Let N1 and N2 such that

• N1 “union” N2 = N

• N1 “intersection” N2 = Φ

C is a set of arcs such that• (n1,n2) A• n1 N1

• n2 N2

D = (N, A)

C is a cut in the digraph D

Page 10: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Cut

n1 n2

n3 n4

10

5

3 2

What is C?

D = (N, A)

N1

N2

{(n1,n2),(n1,n4)} ?

{(n1,n4),(n3,n2)} ?

{(n1,n4)} ?✓

Page 11: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Cut

n1 n2

n3 n4

10

5

3 2

What is C?

D = (N, A)N1N2

{(n1,n2),(n1,n4),(n3,n2)} ?

{(n1,n4),(n3,n2)} ?

{(n4,n3)} ?✓

Page 12: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Cut

n1 n2

n3 n4

10

5

3 2

What is C?

D = (N, A)N2N1

{(n1,n2),(n1,n4),(n3,n2)} ?

{(n1,n4),(n3,n2)} ?

{(n3,n2)} ?

Page 13: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Cut

n1 n2

n3 n4

10

5

3 2

Let N1 and N2 such that

• N1 “union” N2 = N

• N1 “intersection” N2 = Φ

C is a set of arcs such that• (n1,n2) A• n1 N1

• n2 N2

D = (N, A)

C is a cut in the digraph D

Page 14: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of a Cut

n1 n2

n3 n4

10

5

3 2 Sum of length of allarcs in C

D = (N, A)

Page 15: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of a Cut

n1 n2

n3 n4

10

5

3 2 w(C) = Σ(n1,n2) C l(n1,n2)

D = (N, A)

Page 16: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of a Cut

n1 n2

n3 n4

10

5

3 2

What is w(C)?

D = (N, A)

N1

N2

3

Page 17: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of a Cut

n1 n2

n3 n4

10

5

3 2

What is w(C)?

D = (N, A)N1N2

5

Page 18: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of a Cut

n1 n2

n3 n4

10

5

3 2

What is w(C)?

D = (N, A)N2N1

15

Page 19: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

st-Cut

n1 n2

n3 n4

10

5

3 2

A source “s”

C is a cut such that• s N1

• t N2

D = (N, A)

C is an st-cut

s

t

A sink “t”

1 2

7 3

Page 20: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of an st-Cut

n1 n2

n3 n4

10

5

3 2

D = (N, A)s

t

1 2

7 3

w(C) = Σ(n1,n2) C l(n1,n2)

Page 21: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of an st-Cut

n1 n2

n3 n4

10

5

3 2

D = (N, A)s

t

1 2

7 3

What is w(C)?

3

Page 22: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Weight of an st-Cut

n1 n2

n3 n4

10

5

3 2

D = (N, A)s

t

1 2

7 3

What is w(C)?

15

Page 23: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Minimum Cut Problem

n1 n2

n3 n4

10

5

3 2

D = (N, A)s

t

1 2

7 3

Find a cut with theminimum weight !!

C* = argminC w(C)

Page 24: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

[Slide credit: Andrew Goldberg]

Augmenting Path and Push-Relabel

n: #nodes

m: #arcs

U: maximumarc length

Solvers for the Minimum-Cut Problem

Page 25: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Outline

• Minimum Cut Problem

• Submodular Energy Functions

Hammer, 1965; Kolmogorov and Zabih, 2004

Page 26: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Overview

Energy Q

DigraphD

One nodes per element

N = N1 U N2

ComputeMinimum

Cut

+ Additional nodes “s” and “t”

Optimalsolution

na N1 implies xa = 0

na N2 implies xa = 1

Page 27: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Outline

• Minimum Cut Problem

• Submodular Energy Functions• Unary Potentials• Pairwise Potentials

Page 28: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

P

Q

xa = 0

xa = 1

Page 29: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

xa = 0

xa = 1

Page 30: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P ≥ Q

P-Q

0

Q

Q+

ConstantP-Q

xa = 0

xa = 1

Page 31: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P ≥ Q

P-Q

0

Q

Q+

ConstantP-Q

xa = 1

w(C) = 0

xa = 0

xa = 1

Page 32: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P ≥ Q

P-Q

0

Q

Q+

ConstantP-Q

xa = 0

w(C) = P-Q

xa = 0

xa = 1

Page 33: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P < Q

0

Q-P

P

P+

Constant

Q-P

xa = 0

xa = 1

Page 34: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P < Q

0

Q-P

P

P+

Constant

xa = 1

w(C) = Q-P

Q-P

xa = 0

xa = 1

Page 35: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Unary Potentials

na

P

Q

s

t

Let P < Q

0

Q-P

P

P+

Constant

xa = 0

w(C) = 0

Q-P

xa = 0

xa = 1

Page 36: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Outline

• Minimum Cut Problem

• Submodular Energy Functions• Unary Potentials• Pairwise Potentials

Page 37: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

P R

Q S

xa = 0 xa = 1

xb = 0

xb = 1

0 0

Q-P Q-P

0 S-Q

0 S-Q

0 R+Q-S-P

0 0+ + +

P P

P P

Page 38: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

na nb

P R

Q S

0 0

Q-P Q-P

0 S-Q

0 S-Q

0 R+Q-S-P

0 0+ + +

P P

P P

s

t

Constant

xa = 0 xa = 1

xb = 0

xb = 1

Page 39: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

na nb

P R

Q S

0 0

Q-P Q-P

0 S-Q

0 S-Q

0 R+Q-S-P

0 0+ +

s

tUnary Potential

xb = 1

Q-P

xa = 0 xa = 1

xb = 0

xb = 1

Page 40: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

na nb

P R

Q S

0 S-Q

0 S-Q

0 R+Q-S-P

0 0+

s

t

Unary Potentialxa = 1

Q-PS-Q

xa = 0 xa = 1

xb = 0

xb = 1

Page 41: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

na nb

P R

Q S

0 R+Q-S-P

0 0

s

t

Pairwise Potentialxa = 1, xb = 0

Q-PS-Q

R+Q-S-P

xa = 0 xa = 1

xb = 0

xb = 1

Page 42: Discrete Optimization Lecture 5 – Part 2 M. Pawan Kumar pawan.kumar@ecp.fr Slides available online

Digraph for Pairwise Potentials

na nb

P R

Q S s

t

Q-PS-Q

R+Q-S-P

R+Q-S-P ≥ 0

xa = 0 xa = 1

xb = 0

xb = 1