CSE803 Fall 2014 1

Pattern Recognition Concepts Chapter 4: Shapiro and Stockman How should objects be represented? Algorithms for recognition/matching * nearest neighbors * decision tree * decision functions * artificial neural networks How should learning/training be done?

CSE803 Fall 2014 2

Feature Vector Representation

X=[x1, x2, … , xn], each xj a real number

Xj may be object measurement

Xj may be count of object parts

Example: object rep. [#holes, Area, moments, ]

CSE803 Fall 2014 3

Possible features for char rec.

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Some Terminology

Classes: set of m known classes of objects (a) might have known description for each (b) might have set of samples for each Reject Class: a generic class for objects not in any of the designated known classes Classifier: Assigns object to a class based on features

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Classification paradigms

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Discriminant functions

Functions f(x, K) perform some computation on feature vector x

Knowledge K from training or programming is used

Final stage determines class

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Decision-Tree Classifier Uses subsets of

features in seq. Feature

extraction may be interleaved with classification decisions

Can be easy to design and efficient in execution

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Decision Trees

#holes

moment ofinertia

#strokes #strokes

best axisdirection

#strokes

- / 1 x w 0 A 8 B

01

2

< t t

2 4

0 1

060

90

0 1

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Classification using nearest class mean

Compute the Euclidean distance between feature vector X and the mean of each class.

Choose closest class, if close enough (reject otherwise)

Low error rate at left

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Nearest mean might yield poor results with complex structure

Class 2 has two modes

If modes are detected, two subclass mean vectors can be used

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Scaling coordinates by std dev

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Another problem for nearest mean classification If unscaled, object

X is equidistant from each class mean

With scaling X closer to left distribution

Coordinate axes not natural for this data

1D discrimination possible with PCA

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Receiver Operating Curve ROC

Plots correct detection rate versus false alarm rate

Generally, false alarms go up with attempts to detect higher percentages of known objects

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Confusion matrix shows empirical performance

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Bayesian decision-making

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Normal distribution 0 mean and unit

std deviation Table enables us

to fit histograms and represent them simply

New observation of variable x can then be translated into probability

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Cherry with bruise Intensities at about 750 nanometers

wavelength Some overlap caused by cherry surface

turning away

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Parametric models