Discriminant Filters for Object Recognition

Abstract

This paper presents a technique for using training data to design image filters for appearance-based object recognition. Rather than scanning the image with a single set of filters and using the results to test for the existence of objects, we use many sets of filters and take linear combinations of their outputs. The combining coefficients are optimized in a training phase to encourage discriminability between the filter responses for distinct parts of the object and clutter. Our experiments on three popular filter types show that by using this approach to combine sets of filters whose design parameters vary over a wide range, we can achieve detection performance competitive with that of any individual filter set. This in turn can ease the task of fine-tuning the settings for both the filters and the mechanisms that analyze their outputs.