Image Classification. MSc Image Processing Assignment March 2003. Summary. Introduction Classification using neural networks Perceptron Multilayer perceptron Applications. Introduction. Definition Assignment of a physical object to one of several prespecified categories Unsupervised
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For more details
See Image Processing course
Unsupervised
kmeans
Fuzzy kmean
Pattern recognition
Algebraic
Parametric
Nonparametric
Neural nets
SVM
Bayes
Minimum distance
Knearest neighbour
Decision trees
Classification
Classification
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w1
f
y=f(wi xi + w0)
wn
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Modelx=(x1…xn) input vector
w=(w0…wn) weight vector
f activation function
x1
1 1
x2
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w0=1
1 1
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x1
w1=1
w1=1
sign
w2=1
x2
x2
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1+x1+x2=0
x1

Perceptron (2)Problem when
Data nonlinearly separable
For more details
Matlab classification toolbox http://tiger.technion.ac.il/~eladyt/Classification_toolbox.html
outputs
inputs
Input layer
1st hidden layer
2nd hidden layer
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8 features
10 neurons
10 neurons
10 neurons
10 neurons
MLP demo (2)For more details
See our program
To go further
See Neural Network Toolbox, demo on generalisation