Invariant object recognition based on a neural network of cascaded RCE nets
A neural network of cascaded restricted Coulomb energy (RCE) networks is constructed for object recognition. A number of RCE networks are cascaded together to form a classifier where the overlapping decision regions in a previously learned network are solved by the next network. The similarities among objects which have complex decision boundaries in the feature space are resolved by this multinetworks approach. The generalization ability of a RCE network recognition system, referring to the ability of the system to correctly recognize a new pattern even when the number of learning exemplars is small, is increased by the proposed coarse-to-fine learning strategy. A new feature extraction technique is proposed for mapping the geometrical shape information of an object into an ordered feature vector of fixed length which is the required form for input to this neural network