A comparison of generic machine learning algorithms for image classification

In this paper, we evaluate 7 machine learning algorithms for image classification including our recent approach that combines building of ensembles of extremely randomized trees and extraction of sub-windows from the original images. For the approach to be generic, all these methods are applied directly on pixel values without any feature extraction. We compared them on four 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 generic methods can come remarkably close to specialized methods. In particular, our sub-window algorithm is competitive with the state of the art, a remarkable result considering its generality and conceptual simplicity.