Introduction to Pattern Recognition Systems

Introduction to Pattern Recognition Systems

• Learning by experience is a key human capability
• Recognizing digits, characters (size, case, rotation, occlusion) is possible
• Humans are fascinated by recognizing patterns in nature and trying to understand these patterns
• Generalizing experiences to make machines learn is a challenging task
• Creating machines that make decisions and learn from experience is a fundamental principle
• Pattern recognition systems have advanced significantly
• Theoretical research in statistics for various models was prominent in early days
• Computer technology advances lead to increased practical applications
• Now, pattern recognition is a core part of machine intelligence and decision-making systems
• Various mathematical/statistical techniques are used

Pattern Recognition

• Categorizing input data into identifiable classes based on significant features.
• Focus on extracting features from a background of irrelevant data
• Pattern recognition aims at identifying patterns in data, classifying data, and determining characteristics
• A pattern is the opposite of chaos, defined vaguely, and given a name
• Examples of patterns include fingerprints, handwritten words, faces, and speech signals.
• Wide range of applications including biology, psychology, medicine, marketing, AI, computer vision, and remote sensing
• Key aspects for a pattern recognition system include data representation, classification, and prototyping

Pattern Recognition Approaches

• Four main approaches:
  • Template matching
  • Statistical classification
  • Syntactic matching
  • Neural networks
• Template matching: Comparing input to stored templates/prototypes
• Statistical approach: Classifies patterns based on statistical properties (e.g., random variables)
• Syntactic approach: Represents patterns as a combination of simple sub-patterns
• Neural networks approach: Emphasizes interconnections similar to biological neural systems

Template Matching

• Simple and early approach to pattern recognition
• Matching input to stored templates/prototypes to assess similarity
• Takes into account allowable operations (translation, rotation, scaling)
• Matching involves determining similarity between two entities, like points, curves, or shapes

Statistical Pattern Recognition

• Assumes statistical basis for classification
• Creates random parameters to represent pattern properties
• Determines the class (category) to which input belongs
• Uses statistical methodologies (e.g., hypothesis testing, correlation)
• Employs models like the Bayesian classifier for implementation

Syntactic Pattern Recognition

• Focus on inter-relationships among features in hierarchical structure
• Represents patterns using symbolic structures (e.g., arrays, strings, trees, graphs)
• Analyzes relationships between components of patterns (similar sub-patterns)
• Compares symbolic representation of input to predefined templates to measure similarity

Neural Networks

• Modeled after biological neural systems
• Parallel computing environment
• Neural network approach is considered a parametric model, similar to statistical methods
• Used for pattern recognition, and machine learning systems
• Interconnections of numerous simple processors.

Feature Extraction and Reduction

• Process of selecting relevant features from input data for the system
• Choosing features that are pertinent to the task at hand
• Features can be obtained through mathematical tools or feature extraction algorithms
• Features are represented as continuous, discrete, or binary variables
• Feature extraction may involve measurements and calculations on input data that identify characteristics

Cluster Analysis

• Grouping data points into homogenous clusters/groups
• Points within each group are similar to each other and different from other groups
• Similarity defined by specific criteria.
• Useful in high-dimensional spaces (difficult natural interpretations)
• Clustering methods found across many fields, including biology, psychology, geology, info retrieval, and pattern recognition
• Cluster analysis is useful in pattern recognition by grouping patterns into similar categories.
• Data reduction, hypothesis generation, hypothesis testing, and predictive modelling

Classifiers Design

• Classifiers partition feature space into regions
• Boundary between regions defines a decision boundary of a classifier.
• Classifiers can be grouped into
  • linear classifiers
  • non-linear classifiers
• Linear classifiers include perceptron algorithm and least square methods that are easier to compute but sometimes are insufficient for non-linearly separable problems
• Non-linear classifiers are required for more complex problems