lec21

Lec21 - Lecture 21 November 5 10 HW5 due Nov 10 Review Parametric classifiers Cascade classifiers using AdaBoost Learning Today PCA and LDA Support

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 1 Lecture 21: November 5, 10 • HW5 due Nov 10 •R e v i e w – Parametric classifiers – Cascade classifiers using AdaBoost Learning •T o d a y – PCA and LDA – Support Vector Machines
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 2 Learning Framework of V&J L 1 L 2 3 samples face T T T F F F Learn weak classifiers from rectangle features Boost strong classifier Bootstrap negative samples Training samples Cascade classifier Weak classifier Layer Cascade
Background image of page 2
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 3 0/1 AdaBoost • Given sample set S ={( x i , y i )}, x i is a feature vector, y i is its label • Initialize sample weights w 1 ,i =1/| S | • For t = 1 : T – For each feature in the feature pool, call the weak learner to get a weak classifier h : -> {0,1} – Select h t with the lowest error e t = E w [| h t ( x i )- y i |] – Update sample weights • Samples with higher errors get a higher weight – Normalize weights to a PDF (add to one) • Output the final ensemble classifier ti i -|h ( )-y | t+ ,i t,i t t t t w= w β , β =e ( -e ) 1 1 1 x   T tt t t t=1 H( )= sgn α h( )-b , α =-log β xx Default 0
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 4 0/1 AdaBoost S W R 1 Feature Pool H= α 1 h 1 R 2 + 2 h 2
Background image of page 4
USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 5 Cascade Learning • Set the max FAR f
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 11/23/2010 for the course CS 574 taught by Professor Ramnevatia during the Fall '10 term at USC.

Page1 / 20

Lec21 - Lecture 21 November 5 10 HW5 due Nov 10 Review Parametric classifiers Cascade classifiers using AdaBoost Learning Today PCA and LDA Support

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online