A case study in using human similarity measure for automated object recognition

Image understanding often involves object recognition, where a basic question is how to decide whether a match is correct. Typically the best match (among a set of prestored objects) is assumed to be the correct match. This may work well in controlled environments (closed world). But, in uncontrolled environments (open world), the test object may not belong to the prestored object classes. In uncontrolled environments, a metric similarity measure (e.g. Euclidean) in conjunction with a threshold is used. However, based on psychophysical studies this is very different from, and far inferior to, human capabilities. To accept or reject a match, we introduce an approach that avoids metric similarity measures and the use of thresholds as it attempts to employ similarity measures used by humans. In the absence of sufficient real data, the approach allows to specifically generate an arbitrarily large number of training exemplars projecting near classification boundary. For aircraft detection, the performance of a neural network trained on such a training set, was comparable to that of a human expert, and far better than a network trained only on the available real data. Furthermore, the results were considerably better than those obtained using a Euclidean discriminator