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Image Classification. MSc Image Processing Assignment March 2003. Summary. Introduction Classification using neural networks Perceptron Multilayer perceptron Applications. Introduction. Definition Assignment of a physical object to one of several pre-specified categories Unsupervised

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image classification

Image Classification

MSc Image Processing Assignment

March 2003

summary
Summary
  • Introduction
  • Classification using neural networks
    • Perceptron
    • Multilayer perceptron
  • Applications
introduction
Introduction
  • Definition
    • Assignment of a physical object to one of several pre-specified categories
  • Unsupervised
  • Supervised

For more details

See Image Processing course

slide4

Supervised

Unsupervised

k-means

Fuzzy k-mean

Pattern recognition

Algebraic

Parametric

Non-parametric

Neural nets

SVM

Bayes

Minimum distance

K-nearest neighbour

Decision trees

Classification

Classification

neural nets
Neural nets
  • Inspired by the human brain
  • Useful for
    • Classification
    • Regression
    • Optimization …
model

x1

.

.

.

.

.

.

w1

f

y=f(wi xi + w0)

wn

xn

Model

x=(x1…xn) input vector

w=(w0…wn) weight vector

f activation function

perceptron

1

-1

w1x1+w2x2+w0=0

Perceptron
  • f=sign
  • 2 inputs
perceptron 2

x1

-1 1

x2

1

w0=1

-1 -1

-1 1

-1

1

x1

w1=1

w1=1

sign

w2=1

x2

x2

+

-1+x1+x2=0

x1

-

Perceptron (2)
  • Example: AND function
perceptron 3
Perceptron (3)
  • Algorithm
    • Minimise set of misclassified examples
    • Gradient ascent
    • Converges if data linearly separable
  • Demo
perceptron 4
Perceptron (4)
  • XOR problem

Problem when

Data non-linearly separable

  • Solution: change activation function

For more details

Matlab classification toolbox http://tiger.technion.ac.il/~eladyt/Classification_toolbox.html

multilayer perceptron mlp
Multilayer Perceptron (MLP)

outputs

  • Able to model complex non-linear functions
  • Hidden layers with neurons
  • Backpropagation algorithm

inputs

mlp 2

y

w0

w1

w2

x1

x2

MLP (2)
  • f=sigmoid
mlp demo
MLP demo
  • Matlab Classification Toolbox
  • Handwritten digits classification
    • Discriminate between 10 digits
mlp demo 2

Output layer

Input layer

1st hidden layer

2nd hidden layer

F

E

A

T

U

R

E

S

O

U

T

P

U

T

8 features

10 neurons

10 neurons

10 neurons

10 neurons

MLP demo (2)
  • Pre-processing
  • Feature extraction
  • Choice of neural network
  • Training
  • Test

For more details

See our program

mlp performance
MLP performance
  • Able to model complex, nonlinear mapping and classification
  • Can be trained by examples, no mathematical description needed
  • In practice, shows good results
mlp limitations
MLP limitations
  • Extensive training data must be available
  • Computation time
  • Curse of dimensionality
    • Generalisation
    • Overfitting

To go further

See Neural Network Toolbox, demo on generalisation

a few applications
A few applications
  • Medicine
  • Defence
  • Radar & Sonar
  • Finance …
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