Image classification
This presentation is the property of its rightful owner.
Sponsored Links
1 / 18

Image Classification PowerPoint PPT Presentation


  • 171 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Image Classification

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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


Image classification

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 …


Thank you

Thank you.


  • Login