Active digit classifiers: A separability optimization approach to emulate cognition

Given sufficient resources, any classification task is possible with a high accuracy, but to achieve a particular task given finite resources, the problem is to utilize these resources intelligently. Cognitive studies in human vision associate multi-resolution features with high recognition accuracy. We show that classifier development using separability optimization is very similar to emulation of human cognition. The identification of key features leads to optimal resource utilization by the classifier. Evolving such classifiers is the focus of this paper. The resources required for classification can be identified in terms of amount of time required to develop a recognizer, amount of processing power required and the number and kind of features extracted. Our digit recognition method strives not only to report high accuracy but also targets generation of simple solutions. The simplicity of a solution can be a measure of the resources utilized. Our methodology is termed as active based on the premise that once the complexity of a classification task is known an intelligent recognizer should incrementally increase the resources needed for classification.