Aim of the talk - We want to illustrate the very particular graphs issued from computer vision techniques. - Noisy - Complex Attributes (continuous, numerical, symbolic, semantic, …) - Graph Size - What we won't talk about : - Graph for image segmentation (Normalized Cut Graph, ...) - Graph for knowledge representation (Ontology, RDF, ...)
Part 1 •
Computer Vision and Graph-Based Representation
Graph of pixels Pixels Edges
The nodes of the graph The values (RGB, grey shades)
[Morris, 1986]
0 255
Image
255
255 0
0
0 0
0 128
128
Attributed graph [Franco, 2003]
255
0
0 0
0
128
Maximum spanning tree
Expensive edge deletion
Problem Graphs made of pixels are often too big to be analysed
Interest Point Graph
Region Adjacency Graph
Neighborghood graph
Region Adjacency Graph
Impact of noise on Graph-Based Representation Herve Locteau : PhD 2008
Strongly attributed graphs • Numerical vectors • Symbolic information
•
Complex structures • From planar graph to complete graph
•
Graph size • From large to small : It depends on the description level • Low level : One node = one pixel • High level: One node = one object
•
Graph corpus • Large data set : • one graph equal one image
IAM DB • Please read the following paper : • IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning •
Pattern Recognition • Classification (supervised) • Clustering (Unsupervised) • Indexing • All these notions will be deeply explained by Nicolas Ragot in details.
What is pattern recognition? “The assignment of a physical object or event to one of several prespecified categeries” -- Duda & Hart
• A pattern is an object, process or event that can be given a name. • A pattern class (or category) is a set of patterns sharing common attributes and usually originating from the same source. • During recognition (or classification) given objects are assigned to prescribed classes. • A classifier is a machine which performs classification.
Basic concepts Pattern
y Hidden state
y ∈Y
⎡ x1 ⎤ ⎢ x ⎥ ⎢ 2 ⎥ = x ⎢ ⎥ ⎢ ⎥ ⎣ xn ⎦
Feature vector
x∈X
- A vector of observations (measurements). - x is a point in feature space X
- Cannot be directly measured. - Patterns with equal hidden state belong to the same class. Task
- To design a classifer (decision rule) q : X → Y which decides about a hidden state based on an onbservation.
.
Example height
Task: jockey-hoopster recognition.
weight
⎡ x1 ⎤ ⎢ x ⎥ = x ⎣ 2 ⎦
The set of hidden state is Y The feature space is
Training examples Linear classifier:
= {H , J } X = ℜ2
{(x1 , y1 ),…, (xl , yl )}
y=H
x2
⎧H if (w ⋅ x) + b ≥ 0 q(x) = ⎨ ⎩ J if (w ⋅ x) + b < 0 y=J
( w ⋅ x) + b = 0
x1
Pattern Recognition
Pattern Recognition
Nearest Neighbor Search
Vector vs Graph Pattern Recognition Structural
Statistical
symbolic data structure
numeric feature vector
Representational strength
Yes
No
Fixed dimensionality
No
Yes
Sensitivity to noise
Yes
No
Efficient computational tools
No
Yes
Data structure
Graph recognition
Pattern Recognition • When using graphs in pattern recognition the question turns often in a graph comparison problem ? • Are two graphs similar or not? • How to compute a similarity measure for graphs ? • Any ideas ?
Pattern Recognition • When using graphs in pattern recognition the question turns often in a graph comparison problem ? • Are two graphs similar or not? • How to compute a similarity measure for graphs ? • Any ideas ? • At least 2 solutions : • Graph matching • Graph embedding
Some clues : Graph Matching
Some clues : Graph Matching • MCS : Stands for Maximum common subgraph
Bibliography • Bibliography : • IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
My CV in English - last update 2006 -. - Researcher Form. - - Google Me - http://romain.raveaux.free.fr - The Home of Romain Raveaux. Powered by Mambo.
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