Humans recognize transformed images from a very small number of samples. Inspired by this idea, we evaluate a classification method that requires only one sample per class, while providing invariance to image transformations generated by a compact group. This method is based on signatures computed for images. We test and illustrate this theory through simulations that highlight the role of image structure and sampling density, as well as how the signatures are constructed. We extend the existing theory to account for variations in recognition accuracy due to image structure.
Keywords: Image classification; Invariance; Signature.