An integrated model for evaluating the amount of data required for reliable recognition

Abstract

Many recognition procedures rely on the consistency of a subset of data features with an hypothesis, as the sufficient evidence to the presence of the corresponding object. The performance of such procedures are analyzed using a probabilistic model and provide expressions for the sufficient size of such data subsets, that, if consistent, guarantee the validity of the hypotheses with arbitrarily prespecified confidence. The analysis focuses on 2D objects and on the affine transformation class, and is based, for the first time, on an integrated model, which takes into account the shape of the objects involved, the accuracy of the data collected, the clutter present in the scene, the class of the transformations involved, the accuracy of the localization, and the confidence required in the hypotheses. Most of these factors can be quantified cumulatively by one parameter, denoted “effective similarity”, which largely determines the sufficient subset size.