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AFAIK you will always have to limit the feature vectors to a standard size, like for example choose the first 50 features. All machine learning approaches demand the same size of input vectors for all images.
Normalizing the feature vector is a logical approach if you want to keep your data in a normalized range, which is done quite often in computer vision. However, this doesn't have to be, it is possible to apply ANN to unnormalized data also. The result however, will be influenced if the data has various value ranges.
