Pattern Recognition

Vidya ManianDept. of Electrical and Computer Engineering

University of Puerto Ricomanian@ece.uprm.edu

INEL 5046, Spring 2007

Human Perception

• Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe, e.g.,– Recognizing a face– Understanding spoken words– Reading handwriting– Distinguishing fresh food from its smell

• We would like to give similar capabilities to machines

What is a Pattern?

• “A pattern is the opposite of a chaos; it is an entity vaguely defined, that could be given a name.” (Watanabe)

What is Pattern Recognition?

• A pattern is an entity, vaguely defined, that could be given a name, e.g.,

> Fingerprint image >speech signal> handwritten word >DNA sequence>human face >…

• Pattern recognition is the study of how machines can– Observe the environment– Learn to distinguish patterns of interest– Make sound and reasonable decisions about the categories of

the patterns

Recognition

• Identification of a pattern as a member of a category we already know, or we are familiar with– Classification (known categories)– Clustering (creation of new categories)

Category “A”

Category “B”

Classification

Clustering

Pattern Recognition

• Given an input pattern, make a decision about the “category” or “class” of the pattern

• Pattern recognition is a very broad subject with many applications

• In this course we will study a variety of techniques to solve P.R. problems and discuss their relative strengths and weaknesses

Pattern Class

• A collection of “similar” (not necessarily identical) objects

• A class is defined by class samples (paradigms, exemplars, prototypes)

• Inter-class variability

• Intra-class variability

Pattern Class Model

• Different descriptions, which are typically mathematical in form for each class/population

• Given a pattern, choose the best-fitting model for it and then assign it to class associated with the model

Intra-class and Inter-class Variability

The letter “T” in different typefaces

Same face under different expression, pose….

Identical twins

Characters that look similar

Inter-class Similarity

Pattern Recognition

• Having been shown a few positive examples (and perhaps a few negative examples) of a pattern class, the system “learns” to tell whether or not a new object belongs in this class (Watanabe)

• COGNITION = Formation of new classes

RECOGNITION = known classes

Pattern Recognition Applications

• Speech recognition• Detection and diagnosis of disease• Remote sensing (terrain classification, tanks

detection)• Character recognition• Identification and counting of cells• Fingerprint identification• Web search• Inspection (PC boards, IC masks, textiles)

Fish Classification

Preprocessing will involve image enhancement, separating touching/occluding fishes and finding the boundary of the fish

Length Feature

Training (design or learning) Samples

Lightness Feature

Overlap in the histograms is small compared to length feature

Two-dimensional Feature Space (Representation)

Two features together are better than individual features

Cost of misclassification?

Complex Decision Boundary

Issue of generalization

Boundary With Good Generalization

Simplify the decision boundary!

Models for Pattern Recognition

• Template matching

• Statistical (geometric)

• Syntactic (structural)

• Artificial neural network (biologically motivated?)

• Hybrid approach

Statistical Pattern Recognition

PreprocessingFeature

extractionClassificati

on

LearningFeature

selection

Recognition

Training

pattern

Patterns+

Class labels

Preprocessing

• Each pattern is represented as a point in the d-dimensional feature space• Features are domain-specific and be invariant to translation, rotation and

scale• Good representation small intraclass variation, large interclass

separation, simple decision rule• No redundant features, too many features and less samples-curse of

dimensionality (Huges phenomena)

Pattern Representation using features

x1

x2

x1

x2

Artificial Neural Networks• Massive parallelism is essential for complex

pattern recognition tasks (e.g., speech and image recognition) – Human take only a few hundred ms for most

cognitive tasks; suggests parallel computation• Biological networks attempt to achieve good

performance via dense interconnection of simple computational elements (neurons)– Number of neurons 1010 – 1012

– Number of interconnections/neuron 103 – 104

– Total number of interconnections 1014

Artificial Neural Networks

• Nodes in neural networks are nonlinear, typically analog

where is internal threshold or offset

x1

x2

xd

Y (output)

w1

wd

• Feed-forward nets with one or more layers (hidden) between the input and output nodes

• A three-layer net can generate arbitrary complex decision regions

• These nets can be trained by back-propagation training algorithm

Multilayer Perceptron

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d inputs First hidden layerNH1 input units

Second hidden layerNH2 input units

c outputs

Statistical Pattern Recognition• Patterns represented in a feature space• Statistical model for pattern generation in feature space

• Given training patterns from each class, goal is to partition the feature space.

Image Analysis and Segmentation (classification) using texture features

Aerial photograph of Anasco,PR Classified using Logical operators

Classification of color images using texture features

Texture mosaic of 3 colored tilesand canvas texture.

Classified image

Classification in Remote Sensing

SensorSensor

Classification of Landsat image of San Juan area, PR using Gabor texture features

Landsat image 7 bands (R, G, B, IR and Thermal)

Classified image using R,G,B