Publications Details
One-class generalization in second-order backpropagation networks for image classification
In an earlier paper, we reported that it is possible to train a first-order multi-layer feedforward network with backpropagation to classify raw 8-bit images of vehicles. We concluded that a linear feedforward network is capable of within-class generalization when trained with perspective views taken every 10{degree}, but it is incapable of one-class generalization. This paper describes the results of a set of experiments to train a feedforward network with second-order inputs to perform one-class classification on image data. We compare the results of the first-order network and the second-order network and show that the second order network is better able to generalize as a one-class classifier. 7 refs., 6 figs.