Pattern Recognition from One Example by Chopping
François Fleuret, Gilles Blanchard · 2005
We investigate the learning of the appearance of an object from a single image of it. Instead of using a large number of pictures of an object to be recognized, we use pictures of other objects to learn invariance to noise and variations in pose and illumination. This acquired knowledge is then used to predict if two images of objects unseen during training actually display the same object. We propose a generic scheme called chopping to address this task. Using a fast learner, we build hundreds of arbitrary binary splits of the image space designed to assign the same label to all the training images of any given object.