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RANDOM WALKS FOR
IMAGE SEGMENTATION

IEEE Transaction on pattern analysis and machine
intelligence, November 2006

Outline

Introduction

Algorithm

Dirichlet

Problem

Behavioral Properties

Result
--
Demo

2

Introduction

K
-
way image segmentation

User
-
defined
seeds
user
-
defined
labels

How to labels and unseeded pixel ?

K objects

3

Introduction

Algorithm is resolving the question:

Given a random walker starting at this location, what is
the probability that it first reaches each of the K seed
points ?

Probability = ? A random walker first reach each of
the K seed points.

K
-
tuple

vector

A pixel

Seed point

Probability?

4

Introduction

5

The probability a random walker first
reaches a seed point.

The solution to the
Dirichelet

problem
with boundary conditions at the locations
of the seed points and the seed point in
question fixed to unity while the others
are set to zero.

Introduction

Goal

1. location of weak (or missing) boundaries.

2. noise robustness.

3. ability to identify multiple objects simultaneously.

4. fast computation (and editing).

5. avoidance of small/trivial solutions.

6

Algorithm

1. generating the graph weights

2. establishing the system of equations to solve the

problem.

3. the practical details of implementation.

7

Defining a graph

Graph

G = ( V , E )

And edge, e, spanning two vertices, v
i

and
v
j
, is
denoted by
e
ij
.

The weight of an edge,
e
ij
, is denoted by
w
(
e
ij
) or
w
ij
.

Degree

of a vertex is

Assume this graph is
connected

and
undirected
.

8

Edge Weights

9

Gaussian weighting function

The only free parameter

g
i

indicates the image
intensity at pixel
i

Combinatorial
Dirichlet

Problem

10

The
Dirichlet

integral

A
harmonic function

is a function that satisfies the
Laplace equation

Dirichlet

problem
-

finding a harmonic function
subject to its boundary values.

Combinatorial
Dirichlet

Problem(cont.)

11

Combinatorial
Laplacian

matrix

Where
L
ij

is indexed by vertices v
i

and
v
j
.

The
m
x
n
edge
-
node
incidence matrix

as

Incidence matrix is indexed by edge
e
ij

and node
v
k
.

Combinatorial
Dirichlet

Problem(cont.)

12

A combinatorial formulation of the
Dirichlet

integral

C is the
m
x
m

constitutive matrix

(the diagonal matrix
with the weights of each edge along the diagonal).

Combinatorial
Dirichlet

Problem(cont.)

13

Partition the vertices into two sets,

V
M

(marked/seed nodes)

V
U

(unseeded nodes)

Finding the critical point yields

Combinatorial
Dirichlet

Problem(cont.)

14

The probability (potential) assumed at node, v
i
, for
each label, s, by .

Define the set of labels for the seed points as a
function

Define the vector for each label, s, at node

as

Solving the combinatorial
Dirichlet

problem

15

For one label

For all labels

X has K columns taken by each and M has
colums

given by each

Equivalences between random walks
and electrical circuits

16

Three fundamental equations of circuit theory.

These three equations may be combined into the
linear system

It is equivalent to with f = 0.

Algorithm Summary

17

1.
Using , map the image
intensities to edge weights in the lattice.

2.
Obtain a set,
V
M
, of marked (labeled) pixels with
K labels, either interactively or automatically.

3.
Solve outright for the potentials or
solve for each label except the
final one, f. Set

4.
Obtain a final segmentation by assigning to each
node, v
i
, the label corresponding to

Overview of segmentation computation

18

Analogies

19

Assigns an unseeded pixel to a label, given a weighted
graph:

If a random walker leaving the pixel is most likely to first
reach a bearing label
s
, assign the pixel to label
s
.

If the seeds are alternately replaced by grounds/unit
voltage sources, assign the pixel to the label for which its
seeds being “on” produces the greatest electrical potential.

Assign the pixel to the label for which its seeds have the
largest
effective conductance
.

If a
2
-
tree

is drawn randomly from the graph, assign the
pixel to the label for which the pixel is most likely to remain
connected to.

Effective Conductance

20

Effective conductance

Dirichlet

integral
equals the effective conductance
between nodes labeled “1” (“on”) and those
labeled “0” (“off”).

x is intended to include both
x
M

and
x
U

i

j

i

j

equals

Unit voltage

Current flow

Effective Conductance (cont.)

21

Effective conductance between two nodes, v
i
,
v
j

is
given by

where T is a set of edges defining a connected tree

the sum is over all possible trees in the graph

Where TT(
i
,
j
) is used to represent the set of edges
defining a
2
-
tree
, such that node v
i

is in one component
and
v
j

is in another.

2
-
tree

22

A
2
-
tree

is defined to be a tree with one edge
removed.

v
i

and
v
j

are indifferent components and
v
t

is in the
same component as
v
j
.

Note that

2
-
tree (cont.)

23

Then, the following expressions are equivalent

The segmentation is computed from the potentials
by assigning the pixel to the label for which it has
greatest potential (probability).

Behavioral Properties

24

1.
Weak Boundary detection

2.
Noise robustness

3.
Assignment of ambiguous regions

Weak Boundaries

25

Weak Boundaries
-

Comparison

26

Noise Robustness

27

Ambiguous Unseeded Regions

28

Demo Videos

29

mage_Segmentation.html

Brain

Lung tumor

Aorta
-
3D

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Definition of a harmonic function

37

Any
real function

u(x, y) with continuous second
partial derivatives

which satisfies
Laplace's
equation
, is called a harmonic
function.

Reference from
Mathworld
: http://mathworld.wolfram.com/HarmonicFunction.html

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