Learning from one example in machine vision by sharing probability densities
Erik G. Miller, Paul A. Viola · 2002
Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object and a model of change learned from a di#erent object, we form a model of the new object that can be used for synthesis, classification, and other visual tasks. We start by