Avoiding metric similarity measures in ATR

In target recognition in uncontrolled environments the test target may not belong to the prestored targets or target classes. Hence, in such environments the use of a typical classifier which finds the closest class still leaves open the question of whether the test target truly belongs to that class. To decide whether a test target matches a stored target, common approaches calculate a degree of similarity between the two targets using a similarity measure such as Euclidean distance, and make a decision based on whether the distance exceeds a (prespecified) threshold. Based on psychophysical studies, this is very different from, and far inferior to, human capabilities. In this paper we show a new approach where a neural network learns a decision boundary between the confirmation vs. rejection of a match with the help of a human critic. The decision boundary is a multidimensional surface, and models the human similarity measure for the recognition task at hand, thus avoiding metric similarity measures and thresholds. A case study in automatic aircraft recognition is shown. In the absence of sufficient real data, the approach allows us to specifically generate an arbitrarily large number of training exemplars projecting near the classification boundary. The performance of the trained network was comparable to that of a human expert, and far better than a network trained only on the available real data. Furthermore, the result were considerably better than those obtained using a Euclidean discriminator.