Synthetic discriminant functions for recognition of images on the boundary of the convex hull of the training set

摘要

In designing synthetic discriminant function (SDF) filters, the usual choice for the correlation constraint is a real-valued constant for all training images of a given class. However, this choice of constraints results in a filter that recognizes all images in the convex hull of the training set, which is generally undesirable. The use of appropriate complex-valued constraints, though, produces an SDF filter which recognizes only images near the boundary of this convex hull and thus provides improved discrimination performance. This improvement in discrimination can be accomplished without degrading the generalization performance of the filter.