complete explanation about Design principle of pattern recognition system and STATISTICAL PATTERN RECOGNITION with example

1. SIR CHHOTU RAM INSTITUTE OF
ENGINEERING AND TECHNOLOGY
Session-2019-20
ASSIGNMENT
Subject:-ARTIFICIAL INTELLIGENCE
Submitted To: Submitted By:
Ms. SAVITA MITTAL MA’AM TEJVEER SINGH
(MCA 4TH sem )

2. Design principle of pattern recognition system
Pattern Recognition | Introduction
Pattern Recognition System
Pattern is everything around in this digital world. A pattern can either be seen
physically or it can be observed mathematically by applying algorithms.
In Pattern Recognition, pattern is comprises of the following two fundamental
things:-
 Collection of observations
 The concept behind the observation
Feature Vector:-
The collection of observations is also known as a feature vector. A feature is a
distinctive characteristic of a good or service that sets it apart from similar
items. Feature vector is the combination of n features in n-dimensional column
vector. The different classes may have different features values but the same class
always has the same features values.
Example:- The colors on the clothes, speech pattern etc.

3. Example:-
 Differentiate between good and bad features.
 Feature properties.
Classifier and Decision Boundaries:-
1. In a statistical-classification problem, a decision boundary is a hypersurface
that partitions the underlying vector space into two sets. A decision boundary is
the region of a problem space in which the output label of a classifier is
ambiguous. Classifier is a hypothesis or discrete-valued function that is used to
assign (categorical) class labels to particular data points.
2. Classifier is used to partition the feature space into class-labeled decision
regions. While Decision Boundaries are the borders between decision

4. regions.
Components in Pattern Recognition System:-
A pattern recognition systems can be partitioned into components. There are five
typical components for various pattern recognition systems. These are as following:-
 A Sensor:- A sensor is a device used to measure a property, such as pressure,
position, temperature, or acceleration, and respond with feedback.
 A Pre-processing Mechanism:- Segmentation is used and it is the process of
partitioning a data into multiple segments. It can also be defined as the
technique of dividing or partitioning a data into parts called segments.
 A Feature Extraction Mechanism:- feature extraction starts from an initial set
of measured data and builds derived values (features) intended to be
informative and non-redundant, facilitating the subsequent learning and
generalization steps, and in some cases leading to better human
interpretations. It can be manual or automated.
 A Description Algorithm :- Pattern recognition algorithms generally aim to
provide a reasonable answer for all possible inputs and to perform “most likely”
matching of the inputs, taking into account their statistical variation
 A Training Set:- Training data is a certain percentage of an overall dataset
along with testing set. As a rule, the better the training data, the better the
algorithm or classifier performs.

5. Design Principles of Pattern Recognition
In pattern recognition system, for recognizing the pattern or structure two basic
approaches are used which can be implemented in different techniques. These are –
 Statistical Approach and
 Structural Approach
Statistical Approach:-
Statistical methods are mathematical formulas, models, and techniques that are
used in the statistical analysis of raw research data. The application of statistical
methods extracts information from research data and provides different ways to
assess the robustness of research outputs.
Two main statistical methods are used:-
1. Descriptive Statistics:- It summarizes data from a sample using indexes such
as the mean or standard deviation.
2. Inferential Statistics:- It draw conclusions from data that are subject to random
variation.
Structural Approach:-
The Structural Approach is a technique wherein the learner masters the pattern of
sentence. Structures are the different arrangements of words in one accepted style
or the other.
Types of structures:-
 Sentence Patterns
 Phrase Patterns
 Formulas
 Idioms

6. Difference Between Statistical Approach and Structural Approach:-
SR. NO. STATISTICAL APPROACH STRUCTURAL APPROACH
1 Statistical decision theory. Human perception and cognition.
2 Quantitative features. Morphological primitives
3 Fixed number of features. Variable number of primitives.
4 Ignores feature relationships. Captures primitives relationships.
5 Semantics from feature position. Semantics from primitives encoding.
6 Statistical classifiers. Syntactic grammars.
Advantages:-
Pattern recognition solves classification problems
Pattern recognition solves the problem of fake bio metric detection.
It is useful for cloth pattern recognition for visually impaired blind people.
It helps in speaker diarization.
We can recognise particular object from different angle.
Disadvantages:
Syntactic Pattern recognition approach is complex to implement and it is very slow
process.
Sometime to get better accuracy, larger dataset is required.
It cannot explain why a particular object is recognized.

7. Example: my face vs my friend’s face.
Applications:-
Image processing, segmentation and analysis
Pattern recognition is used to give human recognition intelligence to machine which
is required in image processing.
Computer vision
Pattern recognition is used to extract meaningful features from given image/video
samples and is used in computer vision for various applications like biological and
biomedical imaging.
Seismic analysis
Pattern recognition approach is used for the discovery, imaging and interpretation of
temporal patterns in seismic array recordings. Statistical pattern recognition is
implemented and used in different types of seismic analysis models.
Radar signal classification/analysis
Pattern recognition and Signal processing methods are used in various applications
of radar signal classifications like AP mine detection and identification.
Speech recognition
The greatest success in speech recognition has been obtained using pattern
recognition paradigms. It is used in various algorithms of speech recognition which
tries to avoid the problems of using a phoneme level of description and treats larger
units such as words as pattern
Finger print identification
The fingerprint recognition technique is a dominant technology in the biometric
market. A number of recognition methods have been used to perform fingerprint
matching out of which pattern recognition approaches is widely used.

8. STATISTICAL PATTERN RECOGNITION
Statistical Pattern Recognition:-
Statistical pattern recognition is now a mature discipline which has been successfully
applied in several application domains. The primary goal in statistical pattern
recognition is classification, where a pattern vector is assigned to one of a finite
number of classes and each class is characterized by a probability density function
on the measured features. A pattern vector is viewed as a point in the
multidimensional space defined by the features. Design of a recognition system
based on this paradigm requires careful attention to the following issues:
Type of classifier
(single-stage vs. hierarchical), feature selection, estimation of classification error,
parametric vs. nonparametric decision rules, and utilizing contextual information.
Current research emphasis in pattern recognition is on designing efficient algorithms,
studying small sample properties of various estimators and decision rules,
implementing the algorithms on novel computer architecture, and incorporating
context and domain-specific knowledge in decision making.
In other word Statistical pattern recognition are defined as
Statistical pattern recognition:-
• In statistical pattern recognition, we use vectors to represent patterns and class
labels from a label set.
• The abstractions typically deal with probability density/distributions of points in
multi-dimensional spaces, trees and graphs, rules, and vectors themselves.
• Because of the vector space representation, it is meaningful to talk of
subspaces/projections and similarity between points in terms of distance measures.
• There are several soft computing tools associated with this notion. Soft computing
techniques are tolerant of imprecision, uncertainty and approximation. These tools
include neural networks, fuzzy systems and evolutionary computation.
• For example, vectorial representation of points and classes are also employed by

9. – neural networks,
– fuzzy set and rough set based pattern recognition schemes.
Representing patterns as vectors
• The most popular method of representing patterns is as vectors.
• Here, the training dataset may be represented as a matrix of size (nxd), where
each row corresponds to a pattern and each column represents a feature.
• Each attribute/feature/variable is associated with a domain. A domain is a set of
numbers, each number pertains to a value of an attribute for that particular pattern.
• The class label is a dependent attribute which depends on the ‘d’ in-dependent
attributes.
Example The dataset could be as follows
In this case, n=7 and d=6. As can be seen,each pattern has six attributes( or
features). Each attribute in this case is a number between 1 and 9. The last number
in each line gives the class of the pattern. In this case, the class of the patterns is
either 1, 2 or 3.
2. If the patterns are two- or three-dimensional, they can be plotted.

10. 3. Consider the dataset
Figure 1: Dataset of three classes
Pattern 1 : (1,1.25,1) Pattern 2 : (1,1,1)
Pattern 3 : (1.5,0.75,1) Pattern 4 : (2,1,1)
Pattern 5 : (1,3,2) Pattern 6 : (1,4,2)
Pattern 7 : (1.5,3.5,2) Pattern 8 : (2,3,2)
Pattern 9 : (4,2,3) Pattern 10 : (4.5,1.5,3)
Pattern 11 : (5,1,3) Pattern 12 : (5,2,3)
Each triplet consists of feature 1, feature 2 and the class label. This is shown in
Figure 1.

11. BIBLIOGRAPHY
1. Savita Mittal Ma’am Notes
2. https://www.geeksforgeeks.org
3. https://www.cet.edu.in