Introduction to Pattern Recognition What was that….?

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

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

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

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

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

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

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

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

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

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

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

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

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