Adaptive selection of image classifiers

Recently, the concept of Multiple Classifier Systems was proposed as a new approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve classification accuracy by combining the outputs of classifiers making uncorrelated errors. Unfortunately, in real image recognition problems, it may be very difficult to design an ensemble of classifiers that satisfies this assumption. In this paper, we propose a different approach based on the concept of adaptive selection of multiple classifiers in order to select the most appropriate classifier for each input pattern. We point out that adaptive selection does not require the assumption of uncorrelated errors, thus simplifying the choice of classifiers forming a Multiple Classifier System. Reported results on the classification of remote-sensing images show that adaptive selection can be used to obtain substantial improvements in classification accuracy.