Design principle of pattern recognition system and STATISTICAL PATTERN RECOGNITION

Design principle of pattern recognition system and STATISTICAL PATTERN RECOGNITION

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  SIR CHHOTU RAMINSTITUTE 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 )
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  Design principle ofpattern 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.
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  Example:-  Differentiate betweengood 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
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  regions. Components in PatternRecognition 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.
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  Design Principles ofPattern 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
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  Difference Between StatisticalApproach 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.
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  Example: my facevs 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.
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  STATISTICAL PATTERN RECOGNITION StatisticalPattern 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
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  – 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.
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  3. Consider thedataset 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.
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  BIBLIOGRAPHY 1. Savita MittalMa’am Notes 2. https://www.geeksforgeeks.org 3. https://www.cet.edu.in