We present a system designed to help in the development of new 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 d u e s . The feature selection subsystem generates an image recognition feature vector by operations on the primitive features. It uses a combination of rulebased techniques and statistical heuristics to select the best features.We propose a quality statistic function which is based on sample values for each primitive feature. Then we decided the parameters of this function . We experimented on several different target image groups using this function. Recognition rates were perfect in each case. We expect the system will work for other image data sets.
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