Optimising Two-Stage Recognition Systems.

A typical recognition system consists of a sequential combination of two experts, called a detector and classifier respectively. The two stages are usually designed independently, but we show that this may be suboptimal due to interaction between the stages. In this paper we consider the two stages holistically, as components of a multiple classifier system. This allows for an optimal design that accounts for such interaction. An ROC-based analysis is developed that facilitates the study of the inter-stage interaction, and an analytic example is then used to compare independently designing each stage to a holistically optimised system, based on cost. The benefit of the proposed analysis is demonstrated practically via a number of experiments. The extension to any number of classes is discussed, highlighting the computational challenges, as well as its application in an imprecise environment. [ABSTRACT FROM AUTHOR]