Aggregated distance metric learning (ADM) for image classification in presence of limited training data

The focus of image classification through supervised distance metric learning is to find an appropriate measure of similarity between images. Although this approach is effective in the presence of large amounts of training data, classification accuracy will deteriorate when the number of training samples is small, which, unfortunately, is often the situation in several medical applications. We present a novel image classification method called aggregated distance metric (ADM) learning for situations where the training image data are limited. Our approach is novel in that it combines the merits of boosted distance metric learning (BDM, a recently published learning scheme) and bagging theory. This approach involves selecting several sub-sets of the original training data to form a number of new training sets and then performing BDM on each of these training sub-sets. The distance metrics learned from each of the training sets are then combined for image classification. We present a theoretical proof of the superiority of classification by ADM over BDM. Using both clinical (X-ray) and non-clinical (toy car) images in our experiments (with altogether 10 sets of different parameters) and image classification accuracy as the measure, our method is shown to be more accurate than BDM and the traditional bagging strategy.