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Image Classification

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 pre-specified categories Unsupervised

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Image Classification

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  1. Image Classification MSc Image Processing Assignment March 2003

  2. Summary • Introduction • Classification using neural networks • Perceptron • Multilayer perceptron • Applications

  3. Introduction • Definition • Assignment of a physical object to one of several pre-specified categories • Unsupervised • Supervised For more details See Image Processing course

  4. Supervised Unsupervised k-means Fuzzy k-mean Pattern recognition Algebraic Parametric Non-parametric Neural nets SVM Bayes Minimum distance K-nearest neighbour Decision trees Classification Classification

  5. Neural nets • Inspired by the human brain • Useful for • Classification • Regression • Optimization …

  6. x1 . . . . . . w1  f y=f(wi xi + w0) wn xn Model x=(x1…xn) input vector w=(w0…wn) weight vector f activation function

  7. 1 -1 w1x1+w2x2+w0=0 Perceptron • f=sign • 2 inputs

  8. x1 -1 1 x2 1 w0=1 -1 -1 -1 1 -1 1 x1 w1=1 w1=1  sign w2=1 x2 x2 + -1+x1+x2=0 x1 - Perceptron (2) • Example: AND function

  9. Perceptron (3) • Algorithm • Minimise set of misclassified examples • Gradient ascent • Converges if data linearly separable • Demo

  10. Perceptron (4) • XOR problem Problem when Data non-linearly separable • Solution: change activation function For more details Matlab classification toolbox http://tiger.technion.ac.il/~eladyt/Classification_toolbox.html

  11. Multilayer Perceptron (MLP) outputs • Able to model complex non-linear functions • Hidden layers with neurons • Backpropagation algorithm inputs

  12. y w0 w1 w2 x1 x2 MLP (2) • f=sigmoid

  13. MLP demo • Matlab Classification Toolbox • Handwritten digits classification • Discriminate between 10 digits

  14. Output layer Input layer 1st hidden layer 2nd hidden layer F E A T U R E S O U T P U T 8 features 10 neurons 10 neurons 10 neurons 10 neurons MLP demo (2) • Pre-processing • Feature extraction • Choice of neural network • Training • Test For more details See our program

  15. MLP performance • Able to model complex, nonlinear mapping and classification • Can be trained by examples, no mathematical description needed • In practice, shows good results

  16. MLP limitations • Extensive training data must be available • Computation time • Curse of dimensionality • Generalisation • Overfitting To go further See Neural Network Toolbox, demo on generalisation

  17. A few applications • Medicine • Defence • Radar & Sonar • Finance …

  18. Thank you.

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