1. IMAN SAUDY UMUT OGUR NORBERT KISS GEORGE TEPES-NICA BARLEY SEEDS CLASSIFICATION

2. Introduction What is SVM? SVM Applications Text Categorization Face Detection The Approach About the Program Test results Conclusions CONTENTS

3. INTRODUCTION Barley seeds image Design a classifier Classes and statistical results

4. WHAT IS SVM? Linear algorithm in a high-dimensional space

5. A separable classification toy problem WHAT IS SVM?

6. Dot product  Polynomial Kernel  RBF Kernel  Sigmoid Kernel WHAT IS SVM?

7. An Example WHAT IS SVM? Classifier Using RBF Kernel

8. 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

9. Text Categorization An Example – Reuters 12,902 Reuters stories, 118 categories 75% to build classifiers 25% to test SVM APPLICATIONS

10. Face Detection MRI OCR SVM APPLICATIONS

11.  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

12.  Consists of two modules:  for training  for testing ABOUT THE PROGRAM

13.  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

14. 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 !!!

15. Team B THANK YOU