ART-EMAP - A NEURAL-NETWORK ARCHITECTURE FOR OBJECT RECOGNITION BY EVIDENCE ACCUMULATION

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and three dimensional (3-D) object recognition from a series of ambiguous two dimensional views, The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and-spatial and temporal evidence integration for dynamic predictive mapping (EMAP), ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages, Stage 1 introduces distributed pattern representation at a view category field, Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory, Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training, Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.