Boosting

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EE462 MLCV
Lecture 5-6
Object Detection
– Boosting
Tae-Kyun Kim
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Face Detection Demo
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Multiclass object detection
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[Torralba et al PAMI 07]
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Object Detection
From TUD dataset
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Object Detection
From TUD dataset
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Object Detection
From TUD dataset
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Number of windows
x
# of scales
# of pixels
Number of Windows: 747,666
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Time per window
……
dimension D
or raw pixels
…
Num of feature
vectors: 747,666
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Time per window
……
dimension D
or raw pixels
…
Num of feature
vectors: 747,666
In order to finish the task in 1 sec
Time per window (or vector):
0.00000134 sec
Neural Network?
Nonlinear SVM?
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Examples of face detection
From Viola, Jones, 2001
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More traditionally…
Narrow down the search space

Integrating Visual Cues [Darrell et al IJCV 00]
 Face pattern detection output (left).
 Connected components recovered from stereo range
data (mid).
 Flesh hue regions from skin hue classification (right).
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Since about 2001…
Boosting Simple Features [Viola &Jones 01]

Adaboost classification
Strong
classifier

Weak
classifier
Weak classifiers: Haar-basis like functions (45,396 in total)
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Introduction to Boosting Classifiers
- AdaBoost
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Sorry for inconsistent notations…
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Boosting
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Boosting
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Boosting
Iteratively reweighting training samples.
 Higher weights to previously misclassified samples.

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21rounds
3
4
5
round
rounds
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AdaBoost
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Boosting
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Boosting as an optimisation framework
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Minimising Exponential Error
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Existence of weak learners


Definition of a baseline learner
Data weights:
Set
Baseline classifier:
Error is at most ½.
for all x
Each weak learner in Boosting is demanded s.t.
→ Error of the composite hypothesis goes to zero as
boosting rounds increase [Duffy et al 00].
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Robust real-time object detector
Boosting Simple Features
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[Viola and Jones CVPR 01]

Adaboost classification
Strong
classifier

Weak
classifier
Weak classifiers: Haar-basis like functions (45,396 in total)
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Evaluation (testing)
From Viola, Jones, 2001
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Boosting Simple Features
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[Viola and Jones CVPR 01]

Integral image
 A value at (x,y) is the sum of the pixel values above
and to the left of (x,y).
 The integral image can be computed in one pass
over the original image.
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Boosting Simple Features
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[Viola and Jones CVPR 01]

Integral image
 The sum of original image values
within the rectangle can be
computed: Sum = A-B-C+D
 This provides the fast evaluation
of Haar-basis like features
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Evaluation (testing)
From Viola, Jones, 2001
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Boosting as a Tree-structured Classifier
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Boosting (very shallow network)

The strong classifier H as boosted decision stumps has a flat
structure
x
……
c0 c1
c0 c1
……
c0 c1
c0 c1
c0 c1
c0 c1

 Cf. Decision “ferns” has been shown to outperform “trees” [Zisserman et al, 07]
[Fua et al, 07]
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Boosting -continued
Good generalisation by a flat structure
 Fast evalution
 Sequential optimisation
A strong boosting classifier

Boosting Cascade [viola & Jones
04], Boosting chain [Xiao et al]
 Very imbalanced tree
 Speeds up for unbalanced
binary problems
 Hard to design
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A cascade of classifiers
 The detection system requires good detection rate and
extremely low false positive rates.
 False positive rate and detection rate are
f_i is the false positive rate of i-th classifier on the examples
that get through to it.
 The expected number of features evaluated is
p_j is the proportion of windows input to i-th classifier.
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Demo video: Fast evaluation
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Object Detection
by a Cascade of Classifiers
Pictures from Romdhani et al. ICCV01
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