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Kullback-Leibler Boosting

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

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Kullback-Leibler Boosting

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

  2. 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

  3. 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

  4. Linear Features • KLBoosting: • VJ Adaboost:

  5. What makes a feature good? • KLBoosting: • RealBoost: • Minimize upper bound on classification error

  6. 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

  7. Example • Initial feature set: x x x x x x x x

  8. Example • Top two features (by KL-Div): x x x x x x x x w1 w2

  9. Example • Initial feature (weighted combo by KL): x x x x x x x x w1 f0 w2

  10. Example • Optimize over w1 x x x x x x x x f1= f0 + B* w1 w1 f1 w2 B = -a1..a1

  11. Example • Optimize over w2 x x x x x x x x f2= f1 + B* w2 w1 f2 w2 B = -a2..a2 (and repeat…)

  12. Creating the feature set First three features Selecting the first feature

  13. Creating feature set

  14. Classification = ½ in RealBoost

  15. Parameter Learning • With each added feature k: • Set first a1..ak-1 to current optimal value • Set ak to 0 • Minimize recognition error on training: • Solve using greedy algorithm

  16. KLBoost vs AdaBoost 1024 candidate features for AdaBoost

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

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

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

  20. Results Test time: .4 sec per 320x240 image x x x x 8 85 853 Schneiderman (2003)

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

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