Automatic Design of Cascaded Classifiers

Cascades of boosted classifiers have become increasingly pop- ular in machine vision and have generated a lot of recent research. Most of it has focused on modifying the underlying Adaboost method and far less attention has been given to the problem of dimensioning the cascade, i.e. determining the number and the characteristics of the boosted classi- fiers. To a large extent, the designer of a cascade must set the parameters in the cascade using ad-hoc methods. We propose to automatically build a cascade of classifiers, given just a family of weak classifiers a desired performance level and little more. First, a boosted classifier with the desired performance is built using any boosting method. This classifier is then "sliced" using dynamic program- ming into a cascade of classifiers in a nearly computation-cost-optimal fashion.

[1]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[2]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

[3]  Harry Shum,et al.  FloatBoost Learning for Classification , 2002, NIPS.

[4]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Takeo Kanade,et al.  Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[7]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[8]  James M. Rehg,et al.  Learning a Rare Event Detection Cascade by Direct Feature Selection , 2003, NIPS.

[9]  R. Bellman.  Dynamic programming. , 1957, Science.

[10]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Javed A. Aslam.  Improving Algorithms for Boosting , 2000, COLT.

[12]  Brendan McCane,et al.  On Training Cascade Face Detectors , 2003 .

[13]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[14]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[15]  Shie Mannor,et al.  Geometric Bounds for Generalization in Boosting , 2001, COLT/EuroCOLT.

[16]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[17]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[18]  Martial Hebert,et al.  Object Recognition by a Cascade of Edge Probes , 2002, BMVC.

[19]  Paul A. Viola,et al.  Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.

[20]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[21]  Cesare Furlanello,et al.  Automatic model selection in cost-sensitive boosting , 2003, Inf. Fusion.