Improved Multiclass Adaboost For Image Classification: The Role Of Tree Optimization

Abstract

Decision tree boosting is considered as an important and widely recognized method in image classification, despite dominance of the deep learning based approaches in this area. Provided with good image features, it can produce a powerful model with unique properties, such as strong predictive power, scalability, interpretability, etc. In this paper, we propose a novel tree boosting framework which capitalizes on the idea of using shallow, sparse and yet powerful oblique decision trees (trained with recently proposed Tree Alternating optimization algorithm) as the base learners. We empirically show that the resulting model achieves better or comparable performance (both in terms of accuracy and model size) against established boosting algorithms such as gradient boosting or AdaBoost in number of benchmarks. Further, we show that such trees can directly and efficiently handle multiclass problems without using one-vs-all strategy employed by most of the practical boosting implementations.