belief propagation on markov random fields aggeliki tsoli

Belief Propagation on Markov Random Fields

Aggeliki Tsoli

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Outline

Graphical Models

Markov Random Fields (MRFs)

Belief Propagation

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Graphical Models

Diagrams Nodes: random variables Edges: statistical dependencies among random

variables

Advantages:1. Better visualization

conditional independence properties new models design

2. Factorization

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Graphical Models types

Directed causal relationships e.g. Bayesian networks

Undirected no constraints imposed on causality of events

(“weak dependencies”) Markov Random Fields (MRFs)

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Example MRF Application: Image Denoising

Question: How can we retrieve the original image given the noisy one?

Original image

(Binary)

Noisy image

e.g. 10% of noise

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MRF formulation Nodes

For each pixel i, xi : latent variable (value in original image) yi : observed variable (value in noisy image) xi, yi {0,1}

x1 x2

xi

xn

y1 y2

yi

yn

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MRF formulation Edges

xi,yi of each pixel i correlated local evidence function (xi,yi) E.g. (xi,yi) = 0.9 (if xi = yi) and (xi,yi) = 0.1 otherwise (10% noise)

Neighboring pixels, similar value compatibility function (xi, xj)

x1 x2

xi

xn

y1 y2

yi

yn

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MRF formulation

Question: What are the marginal distributions for xi, i = 1, …,n?

x1 x2

xi

xn

y1 y2

yi

yn

P(x1, x2, …, xn) = (1/Z) (ij) (xi, xj) i (xi, yi)

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Belief Propagation

Goal: compute marginals of the latent nodes of underlying graphical model

Attributes: iterative algorithm message passing between neighboring latent variables

nodes

Question: Can it also be applied to directed graphs? Answer: Yes, but here we will apply it to MRFs

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1) Select random neighboring latent nodes xi, xj

2) Send message mij from xi to xj

3) Update belief about marginal distribution at node xj 4) Go to step 1, until convergence

1) How is convergence defined?

Belief Propagation Algorithm

xi xj

yi yj

mij

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Message mij from xi to xj : what node xi thinks about the marginal distribution of xj

Step 2: Message Passing

xi xj

yi yj

N(i)\j

mij(xj) = (xi) (xi, yi) (xi, xj) kN(i)\j mki(xi)

Messages initially uniformly distributed

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Step 3: Belief Update

xj

yj

N(j)

b(xj) = k (xj, yj) qN(j) mqj(xj)

Belief b(xj): what node xj thinks its marginal distribution is

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1) Select random neighboring latent nodes xi, xj

2) Send message mij from xi to xj

3) Update belief about marginal distribution at node xj 4) Go to step 1, until convergence

Belief Propagation Algorithm

xi xj

yi yj

mij

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Example

2

- Compute belief at node 1.

1

3

4

m21

m32

m42

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Fig. 12 (Yedidia et al.)

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Does graph topology matter?

BP procedure the same!

Performance Failure to converge/predict accurate beliefs [Murphy,

Weiss, Jordan 1999]

Success at decoding for error-correcting codes [Frey and Mackay

1998] computer vision problems where underlying MRF full of

loops [Freeman, Pasztor, Carmichael 2000]

vs.

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How long does it take?

No explicit reference on paper My opinion, depends on

nodes of graph graph topology

Work on improving the running time of BP (for specific applications) Next time?

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Questions?