Maximum-likelihood and maximum-a-posteriori estimates of human-observer templates

Procedures for direct estimation of observer templates (also known as classification images) have largely focused on unbiased estimates. In this paper we take a different approach, deriving maximum likelihood (ML) and maximum a posteriori (MAP) procedures for estimating an observer template from the outcome of a two-alternative forced-choice experiment. While these estimation procedures will generally result in estimates with some bias, the reduction in variance can potentially outweigh the negative effects of a small bias. One promising feature of the ML and MAP estimates is that the distribution of the sample images used for the experiment is not necessary for evaluating the likelihood term implying that the method is robust to the distribution of images (although the validity of the assumptions used to derive these estimators may not be). It may therefore be possible to use these methods to estimate classification images for detection tasks in realistic images.