IMAN SAUDY UMUT OGUR

NORBERT KISSGEORGE TEPES-NICA

BARLEY SEEDS CLASSIFICATION

Introduction What is SVM? SVM Applications

Text Categorization Face Detection

The Approach About the Program Test results Conclusions

CONTENTS

INTRODUCTION

Barley seeds image Design a classifier Classes and statistical results

WHAT IS SVM?

Linear algorithm in a high-dimensional space

A separable classification toy problem

WHAT IS SVM?

Dot product

Polynomial Kernel

RBF Kernel

Sigmoid Kernel

WHAT IS SVM?

An Example

WHAT IS SVM?

Classifier Using RBF Kernel

Although it constructs models that are complex, it is simple enough to be analyzed mathematically

It can lead to high performances in practical applications

ADVANTAGES

Text Categorization

An Example – Reuters

12,902 Reuters stories, 118 categories

75% to build classifiers

25% to test

SVM APPLICATIONS

Face Detection MRI OCR

SVM APPLICATIONS

Take several images for training (positive/negative)

Tresholding to separate the seed from background

Scale them and sub sample them to minimize the size of the vectors

Feed them to the learning machine model/classifier

THE APPROACH

Consists of two modules:

for training

for testing

ABOUT THE PROGRAM

training set: 28p – 23n

errors:

pos. images recognized as neg. 2-4%

neg. images recognized as pos. 1-2%

training set: 43p – 44n

errors:

pos. images recognized as neg. 0%

neg. images recognized as pos. 0%

TEST RESULTS

CONCLUSIONS

SVMs are a good choice for binary classification (see results in this case)

They can be used no matter what one may want to classify (faces, seeds, etc.)

For in-depth assistance join us for a beer tonight !!!

Team B

THANK YOU