week03-patternRec

7/30/2019 week03-patternRec

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CSE803 Fall 2012 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?


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CSE803 Fall 2012 2

Feature Vector Representation X=[x1, x2, , xn], each xj

a real number

Xj may be objectmeasurement

Xj may be count of objectparts

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


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CSE803 Fall 2012 3

Possible features for char rec.


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CSE803 Fall 2012 4

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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CSE803 Fall 2012 5

Classification paradigms


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CSE803 Fall 2012 6

Discriminant functions Functions f(x, K)

perform some

computation onfeature vector x

Knowledge K fromtraining orprogramming isused

Final stagedetermines class


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CSE803 Fall 2012 7

Decision-Tree Classifier Uses subsets of

features in seq.

Feature extractionmay be interleavedwith classificationdecisions

Can be easy todesign and efficientin execution


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CSE803 Fall 2012 8

Decision Trees

#holes

moment of

inertia#strokes #strokes

best axis

direction #strokes

- / 1 x w 0 A 8 B

0

12

< t t

2 4

0 1

060

90

0 1


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CSE803 Fall 2012 9

Classification using nearest

class mean Compute the Euclidean

distance between

feature vector X and themean of each class.

Choose closest class, ifclose enough (reject

otherwise) Low error rate at left


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CSE803 Fall 2012 10

Nearest mean might yield poor

results with complex structure Class 2 has two

modes

If modes aredetected, twosubclass meanvectors can be

used


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CSE803 Fall 2012 11

Scaling coordinates by std dev


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CSE803 Fall 2012 12

Another problem for nearest

mean classification If unscaled, object X

is equidistant fromeach class mean

With scaling X closerto left distribution

Coordinate axes notnatural for this data

1D discriminationpossible with PCA


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CSE803 Fall 2012 13

Receiver Operating Curve ROC Plots correct

detection rateversus false

alarm rate Generally, false

alarms go upwith attempts todetect higherpercentages ofknown objects


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CSE803 Fall 2012 14

Confusion matrix shows

empirical performance


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CSE803 Fall 2012 15

Bayesian decision-making


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CSE803 Fall 2012 16

Normal distribution

0 mean and unitstd deviation

Table enables us tofit histograms andrepresent themsimply

New observation ofvariable x can thenbe translated intoprobability


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CSE803 Fall 2012 17

Cherry with bruise

Intensities at about 750 nanometers wavelength

Some overlap caused by cherry surface turning away


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CSE803 Fall 2012 18

Parametric Models can beused

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week03-patternRec