Learning models for predicting recognition performance

This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of small gallery size that can give good error estimates and their confidences on large probe sets and populations. We use a generalized two-dimensional prediction model that integrates a hypergeometric probability distribution model with a binomial model explicitly and considers the distortion problem in large populations. We incorporate learning in the prediction process in order to find the optimal small gallery size and to improve its performance. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. During the prediction we use the expectation-maximum (EM) algorithm to learn the match score and the non-match score distributions (the number of components, their weights, means and covariances) that are represented as Gaussian mixtures. By learning we find the optimal size of small gallery and at the same time provide the upper bound and the lower bound for the prediction on large populations. Results are shown using real-world databases