Image Recognition using Region Creep
This paper describes a new type of image classifier that uses a shallow architecture with a very quick learning phase. The image is parsed into smaller areas and each area is saved directly for a region, along with the related output category. When a new image is presented, a direct match with each part is made and the best matching areas returned. These areas can overlap with each other and when moving from a region to its neighbours, there is likely to be only small changes in the area image part. It would therefore be possible to guess what the best image part is for one region by cumulating the results of its neighbours. This is in fact an associative feature of the classifier that can re-construct missing or noisy input by substituting the direct match with what the region match suggests and is being called 'Region Creep'. As each area stores the categories it belongs to, the image classification process sums this to return a preferred category for the whole image. The classifier works mostly at a local level and so to give it some type of global picture, rules are added. These rules work at the whole image level and basically state that if one set of pixels are present, another set should be removed or should also be present. While the rules appear to be very specific, most of the construction can be done automatically. Tests on a set of hand-written numbers have produced state-of-the-art results.