Image Classification

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# Image Classification - PowerPoint PPT Presentation

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

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
• Supervised

For more details

See Image Processing course

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
• Inspired by the human brain
• Useful for
• Classification
• Regression
• Optimization …

x1

.

.

.

.

.

.

w1

f

y=f(wi xi + w0)

wn

xn

Model

x=(x1…xn) input vector

w=(w0…wn) weight vector

f activation function

1

-1

w1x1+w2x2+w0=0

Perceptron
• f=sign
• 2 inputs

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)
• Algorithm
• Minimise set of misclassified examples
• Converges if data linearly separable
• Demo
Perceptron (4)
• XOR problem

Problem when

Data non-linearly separable

• Solution: change activation function

For more details

Multilayer Perceptron (MLP)

outputs

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

inputs

y

w0

w1

w2

x1

x2

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

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
• Able to model complex, nonlinear mapping and classification
• Can be trained by examples, no mathematical description needed
• In practice, shows good results
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
• Medicine
• Defence