Pattern Rejection

Abstract

The efficiency of pattern recognition is particularly crucial in two scenarios; whenever there are a large number of classes to discriminate, and, whenever recognition must be performed a large number of times. We propose a single technique, namely, pattern rejection, that greatly enhances efficiency in both cases. A rejector is a generalization of a classifier, that quickly eliminates a large fraction of the candidate classes or inputs. This allows a recognition algorithm to dedicate its efforts to a much smaller number of possibilities. Importantly, a collection of rejectors may be combined to form a composite rejector, which is shown to be far more effective than any of its individual components. A simple algorithm is proposed for the construction of each of the component rejectors. Its generality is established through close relationships with the Karhunen-Loeve expansion and Fisher's discriminant analysis. Composite rejectors were constructed for two representative applications, namely, appearance matching based object recognition and local feature detection. The results demonstrate substantial efficiency improvements over existing approaches, most notably Fisher's discriminant analysis.