Unsupervised learning of combination features for hierarchical recognition models

Abstract

We propose a cortically inspired hierarchical feedforward model for recognition and investigate a new method for learning optimal combination-coding cells in intermediate stages of the hierarchical network. The model architecture is characterized by weight-sharing, pooling, and Winner-Take-All nonlinearities. We show that an unsupervised sparse coding learning rule can be used to obtain a recognition architecture that is competitive with other more formally abstracted recognition approaches based on supervised learning. We evaluate the performance on object and face databases. © Springer-Verlag Berlin Heidelberg 2002.