Kullback-Leibler Boosting Ce Liu, Hueng-Yeung Shum Microsoft Research Asia CVPR 2003 Presented by Derek Hoiem.

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Presentation transcript:

Kullback-Leibler Boosting Ce Liu, Hueng-Yeung Shum Microsoft Research Asia CVPR 2003 Presented by Derek Hoiem

RealBoost Review Start with some candidate feature set Initialize training sample weights Loop:  Add feature to minimize error bound  Reweight training examples, giving more weight to misclassified examples  Assign weight to weak classifier according to weighted error of training samples  Exit loop after N features have been added

The Basic Idea of KLBoosting Similar to RealBoost except:  Features are general linear projections  Generates optimal features  Uses KL divergence to select features  Finer tuning on coefficients

Linear Features KLBoosting: VJ Adaboost:

What makes a feature good? KLBoosting: RealBoost:  Minimize upper bound on classification error

Creating the feature set Sequential 1-D Optimization  Begin with large initial set of features (linear projections)  Choose top L features according to KL-Div  Initial feature = weighted sum of L features  Search for optimal feature in directions of L features

Example Initial feature set: x x x x x x x x

Example Top two features (by KL-Div): x x x x x x x x w1w1 w2w2

Example Initial feature (weighted combo by KL): x x x x x x x x w1w1 w2w2 f0f0

Example Optimize over w 1 x x x x x x x x w1w1 w2w2 f1f1 f 1 = f 0 + B * w 1 B = -a 1..a 1

Example Optimize over w 2 x x x x x x x x w1w1 w2w2 f2f2 f 2 = f 1 + B * w 2 B = -a 2..a 2 (and repeat…)

Creating the feature set First three features Selecting the first feature

Creating feature set

Classification = ½ in RealBoost

Parameter Learning With each added feature k:  Set first a 1..a k-1 to current optimal value  Set a k to 0  Minimize recognition error on training:  Solve using greedy algorithm

KLBoost vs AdaBoost 1024 candidate features for AdaBoost

Face detection: candidate features 52,400  2,800  450

Face detection: training samples 8760 faces + mirror images 2484 non-face images  1.34B patches Cascaded classifier allows bootstrapping

Face detection: final features top ten global semantic global not semantic local

Results x x x x Schneiderman (2003) Test time:.4 sec per 320x240 image

Comments Training time? Which improves performance:  Generating optimal features?  KL feature selection?  Optimizing alpha coefficients?