Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Recognising Image Shapes from Image Parts, not Neural Parts

Version 1 : Received: 17 January 2022 / Approved: 18 January 2022 / Online: 18 January 2022 (13:11:52 CET)
Version 2 : Received: 10 April 2022 / Approved: 11 April 2022 / Online: 11 April 2022 (10:17:57 CEST)

How to cite: Greer, K. Recognising Image Shapes from Image Parts, not Neural Parts. Preprints 2022, 2022010259 (doi: 10.20944/preprints202201.0259.v1). Greer, K. Recognising Image Shapes from Image Parts, not Neural Parts. Preprints 2022, 2022010259 (doi: 10.20944/preprints202201.0259.v1).

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, where a trick during the scan process can not only recognise separate image parts, as features, but it can also produce an overlap between the parts. It is therefore able to produce image parts with real meaning and also place them into a positional context. Tests show that it can be very accurate, scoring 100% on some handwritten digit test datasets. It also fits well with an earlier cognitive model, and an ensemble-hierarchy structure in particular.

Keywords

image classifier; image part; quick learning; feature overlap; positional context

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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