Ch 6: Adaboost for buildin robust classifiers KH Wong Adaboost, Vga
Ch. 6: Adaboost for building robust classifiers KH Wong Adaboost , V9a 1
Overview Objectives of ada boost Solve 2-class classification problems Will discuss Training Detection Examples Adaboost, Vga
Overview • Objectives of AdaBoost • Solve 2-class classification problems • Will discuss – Training – Detection – Examples Adaboost , V9a 2
Obiective automatically classify inputs into different categories of similar features Example Spam mail detection and filtering Face detection: find the faces in the input image Vision based gesture recognition [chen2007] 3」sp
Objective • Automatically classify inputs into different categories of similar features • Example – Spam mail detection and filtering – Face detection: • find the faces in the input image – Vision based gesture recognition [Chen 2007] Adaboost , V9a 3
Different detection problems Two-class problem will be discussed here E.g. face detection In a picture are there any faces or no face? Multi-class problems(not discussed here Adaboost can be extended to handle multi class problems In a picture are there any faces of men women, children (Still an unsolved problem Adaboost, Vga
Different detection problems • Two-class problem (will be discussed here) – E.g. face detection • In a picture, are there any faces or no face? • Multi-class problems (Not discussed here) – Adaboost can be extended to handle multi class problems • In a picture, are there any faces of men , women, children ? (Still an unsolved problem) Adaboost , V9a 4
Define a 2-class classifier its method and procedures Supervised training Show many positive samples(face) to the system Show many negative samples(non- face) to the system Learn the parameters and construct the final strong classifier. Detection Given an unknown input image the system can tell if there are faces or not Adaboost, Vga
Define a 2-class classifier :its method and procedures • Supervised training – Show many positive samples (face) to the system – Show many negative samples (non-face) to the system. – Learn the parameters and construct the final strong classifier. • Detection – Given an unknown input image, the system can tell if there are faces or not. Adaboost , V9a 5