Classification of 2D and 3D images using pyramid scale decision voting

We introduce a novel classification method for pyramid image representations that is particularly efficient when a small training set is available. A pyramid image representation is usually a unique concatenation of pyramid scale vectors of different discriminatory power. We propose training and classification on a scale by scale basis, i.e., using multiple vectors for the representation of an image. The proposed approach achieves approximately equal performance in comparison to the conventional approach but requires much smaller training sets. It also achieves excellent results for three-dimensional image classification.