A Sequential Learning Resource Allocation Network for Image Processing Applications

Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a new observation occurs, sequential learning algorithms offer the ability to iteratively adapt the existing classifier. In this paper, we present a neural network architecture and a fast online learning algorithm that allow to use the class of resource allocation networks for such adaptive image processing applications. The network is based on receptive fields that are processed by RBF sub-nets. The learning algorithm builds such networks online by adding new units to the sub-nets each time novel input data is observed. For this, we define a global and a local novelty criterion. Experimental results show that the proposed network outperforms existing RAN algorithms when used for face detection and recognition and is competitive with existing classifiers.