1. Introduction to Pattern Recognition
What was that….?

2. Recognition Or Classification
Etymologically, the act of thinking again
Involves “identifying” or “acknowledging”
Classification
Etymologically, the act of separating into groups
Involves “sorting” according to what a thing is called or “associating” to a group
12/9/2018
Copyright, G. A. Tagliarini, PhD

3. Copyright, G. A. Tagliarini, PhD
Recognition
Rigel (900 ly)
Betelgeuse (300 ly)
12/9/2018
Copyright, G. A. Tagliarini, PhD

4. The Classification Process
Input
source
Segmentation
Sensing
Feature Extraction
System Response
Recognition
12/9/2018
Copyright, G. A. Tagliarini, PhD

5. Copyright, G. A. Tagliarini, PhD
Sensing
Depends on the application domain
Consistency can vary widely within and across domains
Must result in a basis for measuring discriminatory features—distinguishing characteristics must be “observable”
12/9/2018
Copyright, G. A. Tagliarini, PhD

6. Segmentation: Extremely Challenging
A required preprocessing step
Examples:
What is the basis for separating components of an image? Color, proximity, boundary contours, “texture”
Where are the boundaries between handwritten letters or words?
When does a spoken word start/stop?
12/9/2018
Copyright, G. A. Tagliarini, PhD

7. Copyright, G. A. Tagliarini, PhD
Feature Extraction
What features are salient for the classification?
Are the features robust?
Do they vary with parameters such as time, frequency, scale, translation, rotation, or proximity?
Do subsets of the features provide classification efficacy?
12/9/2018
Copyright, G. A. Tagliarini, PhD

8. Copyright, G. A. Tagliarini, PhD
Classification
What are the classifier design objectives?
Minimize classification error(s)
Type 1 (reject a true Ho)
Type 2 (fail to reject a false Ho)
Generalization
Reduced computational complexity
Reduced algorithmic complexity
Noise
12/9/2018
Copyright, G. A. Tagliarini, PhD

9. Copyright, G. A. Tagliarini, PhD
System Response
So what?
12/9/2018
Copyright, G. A. Tagliarini, PhD

10. Machine Learning: Creating a Classifier Adaptively
Supervised learning
Feedforward network and backpropagation
Hopfield
Unsupervised learning
ART
Kohonen
12/9/2018
Copyright, G. A. Tagliarini, PhD

11. Copyright, G. A. Tagliarini, PhD
Some Sample Problems
Intrusion detection in network traffic
Handwritten character/word recognition
Speech recognition
Sonar acoustic transient recognition
Face recognition
Fingerprint classification
12/9/2018
Copyright, G. A. Tagliarini, PhD

12. Copyright, G. A. Tagliarini, PhD
Key Questions
What are the examples? (data)
What characteristics distinguish the class exemplars? (features)
How will discriminatory evidence be combined to make a decision? (classifier)
How well does it work? (assessment)
12/9/2018
Copyright, G. A. Tagliarini, PhD

13. Classifier Construction
Data collection or generation
Data may not be abundant or available
Identify features
Determines preprocessing requirements
Choose a classifier to implement
Model may prescribe the classifier
Model may require adaptive construction (training)
Performance Assessment
12/9/2018
Copyright, G. A. Tagliarini, PhD

14. Copyright, G. A. Tagliarini, PhD
No Free Lunch Theorem
Loosely stated, “There is no classifier model that will be optimal for all classification problems.”
12/9/2018
Copyright, G. A. Tagliarini, PhD