Artificial intelligence Pattern recognition system
44 likes20,011 views
This document defines and describes pattern recognition. It discusses what patterns are, pattern recognition systems, the pattern recognition procedure, approaches to pattern recognition including statistical, syntactic/structural, and neural network approaches. It also covers the components of a pattern recognition system including sensing, segmentation/grouping, feature extraction, classification, and post-processing. Finally, it discusses the design cycle for pattern recognition systems including data collection, feature/model choice, training, evaluation, and computational complexity.
Artificial intelligence Pattern recognition system
- 2. • What is pattern? • What is pattern recognition system? • Pattern recognition procedure • Pattern recognition approaches • Pattern recognition system components • The design cycle
- 3. • A set of instances that • share some regularities and similarities • is repeatable • is observable, some time partially, using sensors • May have noise and distortion
- 4. 1) Texture patterns 2) Image objects 3) Speech patterns 4) Text document category patterns 5) Biological signals 6) Many others Texture patterns
- 6. • Pattern recognition (PR) is the scientific discipline that concerns the description and classification(recognition) of patterns(objects) • PR technique are an important component of intelligent systems and are used for many application domains • Decision making • Object and pattern recognition
- 7. • The first step of the procedure extracts data from the input data which characterize the objects • Based on these features, the objects are identified and stored into classes
- 8. • The approaches to pattern recognition developed are divided into two principal areas: decision-theoretic and structural • The first category deals with patterns described using quantitative descriptors, such as length, area, and texture • The second category deals with patterns best described by qualitative descriptors, such as the relational descriptors. 8
- 9. The approaches are: Statistical approach Syntactic and structural approach Neural network approach
- 10. Statistical pattern recognition is based on underlying statistical model of patterns and pattern classes. • Advantages: • 1. The way always combine with other • methods, then it got high accuracy • Disadvantages: • 1.It costs time for counting samples • 2.It has to combine other methods
- 11. • Structural or syntactic PR: pattern classes represented by means of formal structures as grammars, automata, strings, etc. • The aim of structural recognition procedure should not be merely to arrive at a “yes”, “no”, “don’t know” decision but to produce a structural description of the input picture.
- 12. • 1. This method may use to a more • complex structure • 2.It is a good method for character set • 1.Scaling • 2.Rotation • 3.The color is unable to recognize • 4.Intensity
- 13. • classifier is represented as a network of cells modeling neurons of the human brain (connectionist approach). • Pattern recognition can be implemented by using a feed- forward neural network that has been trained accordingly • During training, the network is trained to associate outputs with input patterns
- 14. • When the network is used, it identifies the input pattern and tries to output the associated output pattern
- 15. • Sensing • Segmentation and grouping • Feature extraction • Classification • Post processing
- 16. • Sensing • use of transducer (camera / microphone) • PR system depends on the bandwidth , the resolution sensitivity distortion of the transducer , • Segmentation and grouping • Patterns should be well separated and should not overlap • Feature extraction • aims to create discriminative features goods for classification •
- 17. • A feature extraction example: Feature Input image Classification pattern extraction Apple Banana Solid Liquid
- 18. • Classification • Use a feature vector provided by a feature extractor to assign the object to a category • Post processing • Exploit the context dependent information other than from a target pattern itself to improve performance
- 19. Input sensing segmentation Feature extraction classification Post processing decision
- 20. • Data collection • Feature choice • Model choice • Training • Evaluation • Computational complexity
- 21. • Data collection • How do we know when we have collected an adequately large and representative set of examples for training and testing the system? • Feature choice • Depends on the characteristics of the problem domain . simple to extract , invariant to irrelevant transformation , insensitive to noise • Model choice • Unsatisfied with the performance of one classifier and wants to jump to another class of model
- 22. • Training • Use data to determine the classifier . Many different procedure for training classifiers and choosing models • Evaluation • Measure the error rate • Different feature set • Different training methods • Different training and test data sets • Computational complexity • What is the trade-off between computational ease and performance ? • (How a algorithm scales as a function of the number of features, patterns /categories)
- 23. • http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/r eport.html#Pattern%20Recognition%20-%20an%20example • http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/r eport.html#Pattern%20Recognition%20-%20an%20example