Feature selection for neural network recognition

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Presents a system designed to help in the development of image recognition applications, using a general neural-network classifier and an algorithm for selecting effective image features given a small number of samples. Input to the system consists of a number of primitive image features computed directly from pixel values. The feature selection subsystem generates an image recognition feature vector by operations on the primitive features. It uses a combination of rule-based techniques and statistical heuristics to select the best features. The authors propose a quality statistic function which is based on sample values for each primitive feature. The parameters of this function were decided, and the authors experimented on several different target image groups using this function. Recognition rates were perfect in each case. >

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