Transcript Computer Vision: Edge Detection

Markov Random Fields
Presented by:
Vladan Radosavljevic
Outline
• Intuition
Simple Example
• Theory
Simple Example - Revisited
• Application
• Summary
• References
Intuition
• Simple example
• Observation
– noisy image
– pixel values are -1 or +1
• Objective
– recover noise free image
Intuition
• An idea
• Represent pixels as
random variables
• y - observed variable
• x - hidden variable
x and y are binary
variables (-1 or +1)
• Question:
– Is there any relation
among those
variables?
Intuition
• Building a model
• Values of observed and original pixels should be correlated
(small level of noise) - make connections!
• Values of neighboring pixels should be correlated (large
homogeneous areas, objects) - make connections!
Final model
Intuition
• Why do we need a model?
• y is given, x has to be find
• The objective is to find an image x that maximizes p(x|y)
• Use model
– penalize connected pairs in the model that have opposite sign as they
are not correlated
• Assume distribution
p(x,y) ~ exp(-E)
E=
over all pairs of connected nodes
• If xi and xj have the same sign, the probability will be higher
• The same holds for x and y
Intuition
• How to find an image x which maximizes probability
p(x|y)?
• Assume x=y
• Take a node xi at time and evaluate E for
xi=+1 and xi=-1
• Set xi to the value that has lowest E (highest probability)
• Iterate through all nodes until convergence
• This method finds local optimum
Intuition
• Result
Theory
• Graphical models – a general framework for
representing and manipulating joint distributions
defined over sets of random variables
• Each variable is associated with a node in a graph
• Edges in the graph represent dependencies between
random variables
– Directed graphs: represent causative relationships (Bayesian
Networks)
– Undirected graphs: represent correlative relationships (Markov
Random Fields)
• Representational aspect: efficiently represent complex
independence relations
• Computational aspect: efficiently infer information about data
using independence relations
Theory
• Recall
• If there are M variables in the model each having K
possible states, straight forward inference algorithm
will be exponential in the size of the model (KM)
• However, inference algorithms (whether computing
distributions, expectations etc.) can use structure in
the graph for the purposes of efficient computation
Theory
• How to use information from the structure?
• Markov property: If all paths that connect nodes in set
A to nodes in set B pass through nodes in set C, then
we say that A and B are conditionally independent
given C:
p(A,B|C) = p(A|C)p(B|C)
• The main idea is to factorize joint probability, then
use sum and product rules for efficient computation
Theory
• General factorization
• If two nodes xi and xk are not connected, then they have to
be conditionally independent given all other nodes
• There is no link between those two nodes and all other links
pass through nodes that are observed
• This can be expressed as
• Therefore, joint distribution must be factorized such that
unconnected nodes do not appear in the same factor
• This leads to the concept of clique: a subset of nodes such
that all pairs of nodes in the subset are connected
• The factors are defined as functions on the all possible
cliques
Theory
• Example
• Factorization
where
: potential function on the clique C
Z : partition function
Theory
• Since potential functions have to be positive we can
define them as:
(there is also a theorem that proofs correspondence
of this distribution and Markov Random Fields, E is
energy function)
• Recall
E=
Theory
• Why is this useful?
• Inference algorithms can take an advantage of such
representation to significantly increase computational
efficiency
• Example, inference on a chain
• To find marginal distribution ~ KN
Theory
• If we rearrange the order of summations and
multiplications ~ NK2
Application
Summary
• Advantages
• Graphical representation
• Computational efficiency
• Disadvantages
• Parameter estimation
• How to define a model?
• Computing probability is sometimes difficult
References
[1] Alexander T. Ihler, Sergey Kirshner, Michael Ghilc,
Andrew W. Robertson and Padhraic Smyth
“Graphical models for statistical inference and data
assimilation”, Physica D: Nonlinear Phenomena,
Volume 230, Issues 1-2, June 2007, Pages 72-87