A Hierarchical Classification System Based on Adaptive Resonance Theory

In this study, we propose a hierarchical classification system, which emulates the eye-brain channel in two hierarchical layers. In the first layer, a set of classifiers are trained by using low level, low dimensional features. In the second layer, the recognition results of the first layer are fed to the fuzzy ARTMAP (FAM) classifier which implements the adaptive resonance theory. Experiments indicate that the hierarchical approach proposed in this paper, increases the classification performances compared to the available methods.