II.13 - Hybrid Symbolic-Neural Methods for Improved Recognition Using High-Level Visual Features

Abstract

This chapter discusses hybrid symbolic-neural methods for improved recognition using high-level visual features. Knowledge-based artificial neural networks (KBANN) is a hybrid learning system, making use of both symbolic and neural learning techniques. The approach taken by KBANN is to create knowledge-based neural networks (KNNs) that initially encode in their units and weights, the information contained in a set of symbolic rules. KNNs are made by establishing a mapping between domain theories composed of hierarchical sets of non-recursive, propositional rules and feed forward neural networks. This mapping defines the topology of the KNN as well as its initial link weights. By defining KNNs in this way, problems such as the choice of an initial network topology and the sensitivity of the network to its initial conditions are either eliminated or significantly reduced. Furthermore, unlike ANNs, KNNs have their attention initially focused upon features and combinations of features. Thus, they are less susceptible to spurious correlations in the training data. The experimental results indicate that KBANN can be used to improve roughly-correct information about the recognition of objects given high-level features. The results further indicate that while this roughly-correct information can result in initial performance that is worse than random guessing, its use as a basis for learning can significantly improve generalization by trained networks while reducing the time required for training. No claims about neurological plausibility are made, but it is expected that the approach characterized by KBANN will prove useful in interpreting the information derived by low-level vision systems.