lec21

lec21 - Lecture 21 November 5 10 HW5 due Nov 10 Review...

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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
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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
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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
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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
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USC CS574: Computer Vision, Fall 2010 Copyright 2010, by R. Nevatia 5 Cascade Learning • Set the max FAR f
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lec21 - Lecture 21 November 5 10 HW5 due Nov 10 Review...

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