Recognising Image Shapes from Image Parts, not Neural Parts

Abstract

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method and it is conjectured that this method is more ‘intelligent’ than a traditional neural network. The image parts that it creates not only have more meaning, but they can also be put into a positional context and allow for an explainable result. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network. The fact that it offers an explainable interface however, could make it interesting.