A generic approach for image classification based on decision tree ensembles and local sub-windows

A novel and generic approach for image classification is presented.
The method operates directly on pixel values and does not require
feature extraction. It combines a simple local sub-window extraction
technique with induction of ensembles of extremely randomized
decision trees. We report results on four well known and publicly
available datasets corresponding to representative applications
of image classification problems: handwritten digits (MNIST),
faces (ORL), 3D objects (COIL-100), and textures (OUTEX). A
comparison with studies from the computer vision literature shows
that our method is competitive with the state of the art, an interesting
result considering its generality and conceptual simplicity.
Further experiments are carried out on the COIL-100 dataset to
evaluate the robustness of the learned models to rotation, scaling,
or occlusion of test images. These preliminary results are very
encouraging