Directed spreading activation in multiple layers for low-level feature extraction

Spreading activation neural networks have been proposed in literature. This paper proposes a directed spreading activation neural network model which performs a large number of early vision tasks. It is shown how directed two-dimensional(2D) diffusion followed by detection of local maxima can effectively perform feature extraction, feature centroid determination and feature clustering all on multiple scales in a purely data-driven manner. The feature map, which is the result of this directed spreading activation process can be used in learning and recognition of 2D object shapes from thenbinary patterns invariant to affine transformations.