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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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## PowerPoint Slideshow about ' Image Classification' - brian

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### Image Classification

MSc Image Processing Assignment

March 2003

• Introduction

• Classification using neural networks

• Perceptron

• Multilayer perceptron

• Applications

• Definition

• Assignment of a physical object to one of several pre-specified categories

• Unsupervised

• Supervised

For more details

See Image Processing course

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

• 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

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

• Algorithm

• Minimise set of misclassified examples

• Converges if data linearly separable

• Demo

• XOR problem

Problem when

Data non-linearly separable

• Solution: change activation function

For more details

outputs

• Able to model complex non-linear functions

• Hidden layers with neurons

• Backpropagation algorithm

inputs

w0

w1

w2

x1

x2

MLP (2)

• f=sigmoid

• Matlab Classification Toolbox

• Handwritten digits classification

• Discriminate between 10 digits

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

• Able to model complex, nonlinear mapping and classification

• Can be trained by examples, no mathematical description needed

• In practice, shows good results

• Extensive training data must be available

• Computation time

• Curse of dimensionality

• Generalisation

• Overfitting

To go further

See Neural Network Toolbox, demo on generalisation

• Medicine

• Defence