Reducing JointBoost-Based Multiclass Classification to Proximity Search

Boosted one-versus-all (OVA) classifiers are commonly used in multiclass problems, such as generic object recognition, biometrics-based identification, or gesture recognition. JointBoost is a recently proposed method where OVA classifiers are trained jointly and are forced to share features. JointBoost has been demonstrated to lead both to higher accuracy and smaller classification time, compared to using OVA classifiers that were trained independently and without sharing features. However, even with the improved efficiency of JointBoost, the time complexity of OVA-based multiclass recognition is still linear to the number of classes, and can lead to prohibitively large running times in domains with a very large number of classes. In this paper, it is shown that JointBoost-based recognition can be reduced, at classification time, to nearest neighbor search in a vector space. Using this reduction, we propose a simple and easy-to-implement vector indexing scheme based on principal compon...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Alexandra Stefan (University of Texas at Arlington), Vassilis Athitsos (University of Texas At Arlington), Quan Yuan (Boston University), Stan Sclaroff (Boston University)
• Keywords: AdaBoost, Computer Vision, CVPR 2009, Face Recognition, Gesture Recognition, Multiclass Recognition, One-versus-all Classification