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Rounding-based Moves for Metric Labeling. M. Pawan Kumar École Centrale Paris INRIA Saclay , Île-de-France. Metric Labeling. Variables V = { V 1 , V 2 , …, V n }. Metric Labeling. Variables V = { V 1 , V 2 , …, V n }. Metric Labeling. w ab d (f(a),f(b)). θ b (f(b)). w ab ≥ 0.

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rounding based moves for metric labeling

Rounding-based Movesfor Metric Labeling

M. Pawan Kumar

ÉcoleCentrale Paris

INRIA Saclay, Île-de-France

metric labeling
Metric Labeling

Variables V= { V1, V2, …, Vn}

metric labeling1
Metric Labeling

Variables V= { V1, V2, …, Vn}

metric labeling2
Metric Labeling

wabd(f(a),f(b))

θb(f(b))

wab ≥ 0

θa(f(a))

d is metric

Va

Vb

minf

E(f)

+ Σ(a,b)wabd(f(a),f(b))

= Σaθa(f(a))

Labels L= { l1, l2, …, lh}

Variables V= { V1, V2, …, Vn}

Labeling f: { 1, 2, …, n}  {1, 2, …, h}

metric labeling3
Metric Labeling

Va

Vb

minf

E(f)

+ Σ(a,b)wabd(f(a),f(b))

= Σaθa(f(a))

NP hard

Low-level vision applications

outline
Outline
  • Approximate Algorithms
  • Comparison
  • Rounding-based Moves
  • Conclusion
slide8

Boykov, Veksler and Zabih

Efficiency

Move-Making Algorithms

Kleinberg and Tardos

Accuracy

Convex Relaxations

slide9

Kolmogorov and Zabih

Efficiency

Move-Making Algorithms

Chekuri, Khanna,

Naor and Zosin

Accuracy

Convex Relaxations

outline1
Outline
  • Approximate Algorithms
    • Move-Making Algorithms
    • Linear Programming Relaxation
  • Comparison
  • Rounding-based Moves
  • Conclusion
move making algorithms
Move-Making Algorithms

Space of All Labelings

f

expansion algorithm
Expansion Algorithm

Variables take label lα or retain current label

Boykov, Veksler and Zabih, 2001

Slide courtesy PushmeetKohli

expansion algorithm1
Expansion Algorithm

Variables take label lα or retain current label

Tree

Ground

House

Status:

Initialize with Tree

Expand Ground

Expand House

Expand Sky

Sky

Boykov, Veksler and Zabih, 2001

Slide courtesy PushmeetKohli

multiplicative bounds
Multiplicative Bounds

f*: Optimal Labeling

f: Estimated Labeling

Σaθa(f(a)) + Σ(a,b)wabd(f(a),f(b))

Σaθa(f*(a)) + Σ(a,b)wabd(f*(a),f*(b))

multiplicative bounds1
Multiplicative Bounds

f*: Optimal Labeling

f: Estimated Labeling

Σaθa(f(a)) + Σ(a,b)wabd(f(a),f(b))

B

Σaθa(f*(a)) + Σ(a,b)wabd(f*(a),f*(b))

outline2
Outline
  • Approximate Algorithms
    • Move-Making Algorithms
    • Linear Programming Relaxation
  • Comparison
  • Rounding-based Moves
  • Conclusion
integer linear program
Integer Linear Program

Minimize a linear function over a set of feasible solutions

Indicator xa(i)  {0,1} for each variable Va and label li

Indicator xab(i,k)  {0,1} for each neighbor (Va,Vb) and labels li, lk

Number of facets grows exponentially in problem size

linear programming relaxation
Linear Programming Relaxation

Indicator xa(i)  {0,1} for each variable Va and label li

Indicator xab(i,k)  {0,1} for each neighbor (Va,Vb) and labels li, lk

Schlesinger, 1976; Chekuri et al., 2001; Wainwright et al., 2003

linear programming relaxation1
Linear Programming Relaxation

Indicator xa(i)  [0,1] for each variable Va and label li

Indicator xab(i,k)  [0,1] for each neighbor (Va,Vb) and labels li, lk

Schlesinger, 1976; Chekuri et al., 2001; Wainwright et al., 2003

approximation factor
Approximation Factor

x*: LP Optimal Solution

x: Estimated Integral Solution

ΣaΣiθa(i)xa(i) + Σ(a,b)Σ(i,k) wabd(i,k)xab(i,k)

ΣaΣiθa(i)x*a(i) + Σ(a,b)Σ(i,k) wabd(i,k)x*ab(i,k)

approximation factor1
Approximation Factor

x*: LP Optimal Solution

x: Estimated Integral Solution

ΣaΣiθa(i)xa(i) + Σ(a,b)Σ(i,k) wabd(i,k)xab(i,k)

F

ΣaΣiθa(i)x*a(i) + Σ(a,b)Σ(i,k) wabd(i,k)x*ab(i,k)

outline3
Outline
  • Approximate Algorithms
  • Comparison
  • Rounding-based Moves
  • Conclusion
theoretical guarantees
Theoretical Guarantees

M = ratio of maximum and minimum non-zero distance

outline4
Outline
  • Approximate Algorithms
  • Comparison
  • Rounding-based Moves
  • Conclusion
interval rounding
Interval Rounding

Treat xa(i)  [0,1] as probability that f(a) = i

Cumulative probability ya(i) = Σj≤ixa(j)

ya(2)

ya(i)

ya(k)

0

ya(1)

ya(h) = 1

Choose an interval of length h’

interval rounding1
Interval Rounding

Treat xa(i)  [0,1] as probability that f(a) = i

Cumulative probability ya(i) = Σj≤ixa(j)

r

0

ya(k)-ya(i)

REPEAT

Choose an interval of length h’

Generate a random number r  (0,1]

Assign the label next to r if it is within the interval

example
Example

0.25

0.5

0.75

1.0

ya(2)

ya(3)

0

ya(1)

ya(4)

0.7

0.8

0.9

1.0

yb(1)

yb(3)

0

yb(4)

yb(2)

0.2

0.3

0.1

1.0

0

yc(3)

yc(2)

yc(4)

yc(1)

example1
Example

0.25

0.5

r

ya(2)

0

ya(1)

0.7

0.8

r

yb(1)

0

yb(2)

0.2

0.1

r

0

yc(2)

yc(1)

example2
Example

0.25

0.5

0.75

1.0

ya(2)

ya(3)

0

ya(1)

ya(4)

0.7

0.8

0.9

1.0

yb(1)

yb(3)

0

yb(4)

yb(2)

0.2

0.3

0.1

1.0

0

yc(3)

yc(2)

yc(4)

yc(1)

example3
Example

0.2

0.3

0.1

1.0

0

yc(3)

yc(2)

yc(4)

yc(1)

example4
Example

0.1

0.2

r

yc(3)

yc(2)

0

-yc(1)

-yc(1)

example5
Example

0.25

0.5

0.75

1.0

ya(2)

ya(3)

0

ya(1)

ya(4)

0.7

0.8

0.9

1.0

yb(1)

yb(3)

0

yb(4)

yb(2)

0.2

0.3

0.1

1.0

0

yc(3)

yc(2)

yc(4)

yc(1)

key observation
Key Observation

If d is submodular

d(i,k) + d(i+1,k+1) ≤ d(i,k+1) + d(i+1,k), for all i, k

energy can be minimized via minimum cut

Schlesinger and Flach, 2003

interval move
Interval Move

Choose an interval of length h’

Va

Vb

θab(i,k) = wabd(i,k)

interval move1
Interval Move

Choose an interval of length h’

Add the current labels

Va

Vb

θab(i,k) = wabd(i,k)

interval move2
Interval Move

Choose an interval of length h’

Add the current labels

d’(i,k) ≥ d(i,k)

d’ is submodular

Solve to update labels

Va

Vb

Repeat until convergence

θab(i,k) = wabd’(i,k)

interval move3
Interval Move

Each problem can be solved using minimum cut

Same multiplicative bound as interval rounding

Multiplicative bound is tight

outline5
Outline
  • Approximate Algorithms
  • Comparison
  • Rounding-based Moves
  • Conclusion
theoretical guarantees1
Theoretical Guarantees

M = ratio of maximum and minimum non-zero distance

slide40

Boykov, Veksler and Zabih

Length of interval = 1

Move-Making Algorithms

Kleinberg and Tardos

Length of interval = 1

Convex Relaxations

slide41

Boykov, Veksler and Zabih

Length of interval = 1

Move-Making Algorithms

Chekuri, Khanna,

Naor and Zosin

Optimal interval length

Convex Relaxations

theoretical guarantees2
Theoretical Guarantees

M = ratio of maximum and minimum non-zero distance

questions

Questions?

http://cvn.ecp.fr/personnel/pawan

[email protected]

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