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.

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