Decision Trees and Random Subwindows for Object Recognition

In this paper, we compare five tree-based machine learning methods within our recent generic image-classification framework based on random extraction and classification of subwindows. We evaluate them on three publicly available object-recognition datasets (COIL-100, ETH-80, and ZuBuD). Our comparison shows that this general and conceptually simple framework yields good results when combined with ensembles of decision trees, especially when using Tree Boosting or Extra-Trees. The latter is particularly attractive in terms of computational efficiency.