Boosted scene categorization approach by adjusting inner structures and outer weights of weak classifiers

Abstract

Scene categorization plays an important role in computer vision and image content understanding. It is a multi-class pattern classification problem. Usually, multi-class pattern classification can be completed by using several component classifiers. Each component classifier carries out discrimination of some patterns from the others. Due to the biases of training data, and local optimal of weak classifiers, some weak classifiers may not be well trained. Usually, some component classifiers of a weak classifier may be not act well as the others. This will make the performances of the weak classifier not as good as it should be. In this paper, the inner structures of weak classifiers are adjusted before their outer weights determination. Experimental results on three AdaBoost algorithms show the effectiveness of the proposed approach. © 2011 Springer-Verlag Berlin Heidelberg.