AdaBoost算法及应用PPT课件

P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR 2001. P. Viola and M. Jones. Robust real-time face detection. IJCV 57(2), 2004.
G i v e e r r o r c l a s s i f i e d p a t t e r n s m o r e c h a n c e f o r l e a r n i n g .
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The AdaBoost Algorithm
Given: ( x 1 ,y 1 ) ,, ( x m ,y m ) w h e r e x i X ,y i { 1 , 1 }
slightly better than random
Hh(Tx(,xf,)p,s)ign10Tpf其 (txh他 )t (xp)
t1
训练一个弱分类器（特征f）
就是在当前权重分布的情况下，
确定f 的最优阈值以及不等号的
方 f）s向t对，r所o使n有得g训这c练个la样弱ss本分if的类ie分器r类（误特差征
AdaBoost & Its Applications
1
Outline
Overview The AdaBoost Algorithm How and why AdaBoost works? AdaBoost for Face Detection
2
Overview
AdaBoost & Its Applications
• Find classifier ht :X{1,1}which minimizes error wrt Dt ,i.e.,
m
h targm inj w herej D t(i)[yihj(xi)] m in im izew eig h tederro r
hj
i 1
ቤተ መጻሕፍቲ ባይዱ
• Weight classifier:
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Boosting illustration
Weak Classifier 3
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Boosting illustration
Final classifier is a combination of weak classifiers
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AdaBoost for Face Detection
AdaBoost & Its Applications
t
1 2
ln 1t t
fo rm in im iz e e x p o n e n tia llo s s
• Update distribution: D t 1 ( i) D t( i)e x p [ Z tty ih t(x i) ],Z tisf o rn o r m a liz a tio n
AdaBoost & Its Applications
8
The AdaBoost Algorithm
Given: ( x 1 ,y 1 ) ,, ( x m ,y m ) w h e r e x i X ,y i { 1 , 1 }
Initialization: D 1(i)m 1,i1, ,m D t( i ) : p r o b a b i l i t y d i s t r i b u t i o n o f x i's a tt i m e t For t 1, ,T :
最低。
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The Strong Classifiers
h1(x){1,1} h2(x){1,1}
...
hT(x){1,1}
weak classifiers
slightly better than random
HT(x)sign T tht(x)
t1
strong classifier
7
The AdaBoost Algorithm
3
Introduction
AdaBoost
Adaptive Boosting A learning algorithm
Building a strong classifier a lot of weaker ones
4
AdaBoost Concept
h1(x){1,1} h2(x){1,1}
...
17
The Task of Face Detection
Many slides adapted from P. Viola
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The Viola/Jones Face Detector
2001年，Viola和Jones利用类Haar特征构造弱分类器， 使用AdaBoost算法把弱分类器组合成强分类器，采用 Cascade结构把强分类器串联组合成级联分类器，实现 了准实时的人脸检测。
t1
10
Boosting illustration
Weak Classifier 1
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Boosting illustration
Weights Increased
12
Boosting illustration
Weak Classifier 2
13
Boosting illustration
Weights Increased
• Weight classifier:
t
1 2
ln 1t t
• Update distribution: D t 1 ( i) D t( i)e x p [ Z tty ih t(x i) ],Z tisf o rn o r m a liz a tio n
Output final classifier: signH(x) T tht(x)
Initialization: D 1(i)m 1,i1, ,m For t 1, ,T :
• Find classifier ht :X{1,1}which minimizes error wrt Dt ,i.e.,
m
h targm inj w herej D t(i)[yihj(xi)]
hj
i 1
hT(x){1,1}
weak classifiers
slightly better than random
HT(x)sign T tht(x)
t1
strong classifier
5
Weaker Classifiers
h1(x){1,1} h2(x){1,1}
...
hT(x){1,1}
weak classifiers