Abstract

A classifier using self-organizing maps (SOM) as feature de- tectors is a more accurate representation of how the biological visual system works in terms of its learning mechanism. Each SOM feature de- tector is dened over a limited domain of viewing condition, such that its nodes instantiate the presence of an object. The weights of the SOM nodes are trained via competition, similar to the development of the vi- sual system. We argue that to approach human pattern recognition per- formance, we must look for a more accurate model of the visual system, not only in terms of the architecture, but also on the way how the node connections are developed, such as that of the SOM's feature detectors. This work characterizes SOM as feature detectors to test the similarity of its response vis-a-vis the response of the biological visual system, and to benchmark its performance vis-a-vis the performance of the traditional feature detector convolution lter. We use various input environments i.e. inputs with limited patterns, inputs with various input perturbation and inputs with complex objects, as test cases for evaluation.