AN INTEGRATED MODEL FOR EVALUATING THE AMOUNT OF DATA REQUIRED FOR RELIABLE RECOGNITION

Many recognition procedures rely on the consistency of a subset of dat a features with a hypothesis as the sufficient evidence to the presenc e of the corresponding object. We analyze here the performance of such procedures, using a probabilistic model, and provide expressions for the sufficient size of such data subsets, that, if consistent, guarant ee the validity of the hypotheses with arbitrary confidence. We focus on 2D objects and the affine transformation class, and provide, for th e first time, an integrated model which takes into account the shape o f the objects involved, the accuracy of the data collected, the clutte r present in the scene, the class of the transformations involved, the accuracy of the localization, and the confidence we would like to hav e in our hypotheses. Interestingly, it turns out that most of these fa ctors can be quantified cumulatively by one parameter, denoted ''effec tive similarity,'' which largely determines the sufficient subset size . The analysis is based on representing the class of instances corresp onding to a model object and a group of transformations, as members of a metric space, and quantifying the variation of the instances by a m etric cover.