Significant Enhancement of Object Recognition Efficiency Using Human Cognition based Decision Clustering

Abstract

It is well known that human can recognize object-pattern better using its temporal description. In this paper both theoretical study and experiments were performed to translate this cognition principle into mathematical formula. In the implementation phase we considered breaking up of temporal data of human face into an assembly of time series data for each of which we obtained a decision as output of the chosen classifier. An assembly of decisions was thus resulted for a single temporal input data which was further judiciously clustered to obtain the final decision. Interestingly, the work also showed that the successive order of the time series data was not needed to be maintained; rather an assembly of randomly chosen multiple test data was important to obtain quite significant level of enhancement of classification accuracy. Thus, it gives new interpretation on temporal data based human cognition. The work also indicated that augmentation of this method with any classifier including those which used decision clustering tree, might yield quite a significant enhancement of recognition efficiency.