Pattern Recognition
CSE 802
Michigan State University
Spring 2008
Pattern Recognition
“The real power of human thinking is based on recognizing patterns. The better computers get at pattern recognition, the more humanlike they will become”. Ray Kurzweil, NY Times, Nov 24, 2003
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)
RecognitionIdentification 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”
ClassificationClustering
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, training/learning samples)
Inter-class variability
Intra-class variability
Pattern Class Model
Different descriptions, which are typically mathematical/statistical 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….
Interclass SimilarityInterclass SimilarityInterclass Similarity
Identical twins
Characters that look similar
Pattern RecognitionHaving 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)
Inferring a generality from a few exemplars
COGNITION = Formation of new classesRECOGNITION = known classes
Pattern Recognition Applications
Alphanumeric charactersOptical scanned imageCharacter recognition (page readers, zip code, license plate)
Tanks, airfieldsVisual, infrared, radar imagesAerial reconnaissance
Terrain forms, vegetation cover
Multispectral imagesNatural resource identification
Types of cardiac conditions, classes of brain conditions
EKG, EEG waveformsDetection and diagnosis of disease
Presence/absence of flaw, type of flaw
Ultrasound, eddy current, acoustic emission waveforms
Non-destructive testing
Spoken words, speaker identity
Speech waveformsSpeech recognition
OutputInputProblem
Pattern Recognition Applications
Occurrence of the word in the database
Query word written by a userOnline handwriting retrieval
Owner of the fingerprint, fingerprint classes
Input image from fingerprint sensors
Fingerprint identification
Text relevant to the userKey words specified by a userWeb search
Identify objects, pose, assembly
3-D images (structured light, laser, stereo)
Manufacturing
Acceptable/unacceptableScanned image (visible, infrared)
Inspection (PC boards, IC masks, textiles)
Type of cellsSlides of blood samples, micro-sections of tissues
Identification and counting of cells
OutputInputProblem
Pattern Recognition System
Challenges
RepresentationMatching
A pattern recognition system involves
TrainingTesting
Difficulties of Representation
John P. Frisby, Seeing. Illusion, Brian and Mind, Oxford University Press, 1980
How should we model a face to account for the large intra-class variability?
Difficulties of Representation
“How do you instruct someone (or some computer) to recognize caricatures in a magazine, let alone find a human figure in a misshapen piece of work?”“A program that could distinguish between male and female faces in a random snapshot would probably earn its author a Ph.D. in computer science.” (Penzias 1989)A representatin could consist of a vector of real-valued numbers, ordered list of attributes, parts and their relations….
Good Representation!
Should have some invariant properties (e.g., w.r.t. rotation, translation, scale…)Account for intra-class variationsAbility to discriminate pattern classes of interestRobustness to noise/occlusionLead to simple decision making (e.g., linear decision boundary)Low cost (affordable)
Pattern Recognition SystemDomain-specific knowledge
Acquisition, representationData acquisition
camera, ultrasound, MRI,….Preprocessing
Image enhancement/restoration, segmentationRepresentation
Features: color, shape, textureDecision making
Statistical/geometric pattern recognitionsyntactic/structural pattern recognitionArtificial neural networks
Post-processing/Context
Pattern Recognition System Performance
Error rate (Prob. of misclassification) on independent test samplesSpeedCostRobustnessReject optionReturn on investment
Fingerprint ClassificationFingerprint Classification
Assign fingerprints into one of pre-specified types
Plain Arch Tented Arch Right Loop Left Loop
Accidental Pocket Whorl Plain Whorl Double Loop
Fingerprint EnhancementFingerprint Enhancement
Noisy image Enhanced image
• To address the problem of poor quality fingerprints
Segmentation: Face DetectionSegmentation: Face Detection
*Theo Pavlidis, http://home.att.net/~t.pavlidis/comphumans/comphuman.htm
Games Magazine, September 2001
Segmentation: Face DetectionSegmentation: Face Detection
Fish Classification
Preprocessing will involve image enhancement, separating touching/occluding fishes and finding the boundary of the fish
Length FeatureTraining (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!
Feature Selection/extraction
• How many features and which ones to use in constructing the decision boundary?
• Some features may be redundant!
• Curse of dimensionality—problems with too many features especially when we have a small number of training samples
Fruit Sorter
cherries
apples
lemons grapefruits
Decision boundaries
diameter
redness
Castleman, Digital Image Processing, Prentice-Hall, 1979
General Purpose P.R. System
• Humans have the ability to switch rapidly and seamlessly between different pattern recognition tasks
• It is very difficult to design a device that is capable of performing a variety of different classification tasks
Cat vs. Dog
Access control for water in areas with water shortage (e.g. Australian outback); wildlife vs. livestockInstall a gate that opens only when livestock enters
Identify livestock using a PR systemRugged outdoor camera captures the imageEdge detection and outline tracingMatch to a library of existing shape templatesOpen the gate when there is a match
Prototype system by Dunn et al., U. South Queensland, Australia. Claim that Sheep & goats can be separated with ~100% accuracy
Sheep Vs. Goat
Vision Systems Design, November 2007 (www.vision-systems.com)
AllowDeny
Supervised Classification
Training samples are labeled
Unsupervised Classification
Training samples are unlabeled
Models for Pattern Recognition
Template matching
Statistical (geometric)
Syntactic (structural)
Artificial neural networks (biologically motivated?)
Hybrid approach
Template Matching
Template
Input scene
Deformable Template: Corpus Callosum SegmentationShape training set Prototype and
variation learning
Prototype registration to the low-level segmented image
Prototype warping
Statistical Pattern Recognition
Preprocessing Feature extraction Classification
LearningFeature selection
Recognition
Training
pattern
Patterns+
Class labels
Preprocessing
Each pattern is represented as a point in the d-dimensional feature spaceFeatures and their desired invariance properties are domain-specific
Good representation leads to small intraclass variation, large interclass separation & simple decision rule
Representation
x1
x2
x1
x2
Invariant Representation
Invariance to
• Translation
• Rotation
• Scale
• Skew
• Deformation
• Color
Structural Patten Recognition
Decision-making when features are non-numeric or structuralDescribe complicated objects in terms of simple primitives and structural relationship
Y
N
M
LT
X
Z
Scene
Object Background
D E
L T X Y Z
M ND E
Syntactic Pattern Recognition
Preprocessing
Primitive, relation
extraction
Syntax, structural analysis
Grammatical, structural inference
Primitive selection
Recognition
Training
pattern
Patterns+
Class labels
Preprocessing
Chromosome Grammars
Terminals:VT={∩,⏐,∪,⎨, }Non-terminals: VN={A,B,C,D,E,F}Pattern Classes:
Median Submedian
Acrocentric Telocentric
Chromosome Grammars
Image of human chromosomes
Hierarchical-structure description of a submedium chromosome
Artificial Neural Networks
Massive parallelism is essential for complex pattern recognition tasks (e.g., speech and image recognition)
Humans take only a few hundred milliseconds for most cognitive tasks; this suggests parallel computation in human brain
Biological networks achieve excellent recognition 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
x1x2
xd
Y (output)
w1
wd
Feed-forward nets with one or more layers (hidden) between the input and output nodesA three-layer net can generate arbitrary complex decision regions
These nets can be trained by back-propagation training algorithm
Multilayer Perceptron
.
.
.
.
.
.
.
..
d inputs First hidden layerNH1 input units
Second hidden layerNH2 input units
c outputs
How m chinfo mation are
y u mi sing
Utilizing Context
Qvest
Constraining the Problem
GRAFFITI’S MODIFIED alphabet is largely based on single pen strokes, starting at the dots. As soon as the pen is lifted from the screen, the letter is immediately translated into normal text. The letter “X” is the exception
Graffiti alphabet
Comparing Pattern Recognition Models
Template MatchingAssumes very small intra-class variabilityLearning is difficult for deformable templates
SyntacticPrimitive extraction is sensitive to noiseDescribing a pattern in terms of primitives is difficult
StatisticalAssumption of density model for each class
Neural NetworkParameter tuning and local minima in learning
In practice, statistical and neural network approaches work well
“Super Classifier”
Pool the evidence from component recognizers (classifier combination, mixture of experts, evidence accumulation)
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.
Approaches to Statistical Pattern Recognition
Bayes DecisionTheory
COMPLETE
"Optimal"Rules
Plug-in Rules
ParametricApproach
Density Estimation
GeometricRules
(K-NN,MLP)
NonparametricApproach
SupervisedLearning
MixtureResolving
ParametricApproach
ClusterAnalysis
(Hard, Fuzzy)
Non-parametric Approach
UnsupervisedLearning
INCOMPLETE
Prior Information
Summary
Pattern recognition is extremely useful forAutomatic decision makingAssisting human decision makers
Pattern recognition is a very difficult problemSuccessful systems have been built in well-constrained domainsNo single technique/model is suited for all pattern recognition problemsUse of object models, constraints, and context is necessary for identifying complex patternsCareful sensor design and feature extraction can lead to simple classifiers
Key Concepts
Pattern classRepresentationFeature extractionFeature selection Invariance (rotation, translation, scale, deformation)PreprocessingSegmentationTraining samplesTest samplesError rateReject rateCurse of dimensionality
Key Concepts
Supervised classificationDecision boundary unsupervised classification (clustering)Density Estimation Cost of misclassification/RiskFeature space partitioningGeneralization/overfittingContextual informationMultiple classifiersPrior knowledge