1 / 18

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

victor-mcpherson
Download Presentation

Image Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related