cse803 fall 2014 1 pattern recognition concepts chapter 4: shapiro and stockman how should objects...
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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?
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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, ]
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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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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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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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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