Categorizing visual stimuli: specification of a neural network architecture

The problem of categorizing visual stimuli on the basis of a hierarchical structure of basic, superordinate, and subordinate categories is addressed. A specification for a simplified neural network architecture that uses a uniform linear measure to determine similarity of common features and dissimilarity of distinctive features is derived. The hierarchy is mapped onto a neural network structure in which input-level cells correspond to activities generated by exemplars and output cells correspond to basic level categories. The network can be used for visual categorization at all three levels of abstraction and for the particular case of recognition. Experimental results on the XOR problem and letter recognition have shown that by introducing similarity and dissimilarity in cell activation the network exhibits superior convergence behavior for the backpropagation algorithm