Looking up similar images and searching for crops are computer vision topics, not large language model (basically text predictor) or image generation ai topics.
Image hashing has been around for quite a while now and there is crop resistant image hashing libraries readily available like this one: pypi.org/project/ImageHash/
It’s basically looking for defining features in images and storing those in an efficient searchable way probably in a traditional database. As long as they are close enough or in the case of a crop, a partial match, it’s a similar image.
over_clox@lemmy.world 10 hours ago
JPEG works in 8x8 pixel blocks, and back in the day, most JPEG images weren’t all that big. Each 8x8 pixel block (64 pixels per block) could easily and quickly be processed as if it were a single pixel.
So if you had a 1024x768 JPEG, then the fast scanning technique would only scan the 128x96 blocks, not necessary to process every single pixel.
Of course the results could never be perfectly accurate, but most images are unique enough that this would be more than sufficient for fast scanning.
bathing_in_bismuth@sh.itjust.works 10 hours ago
Okay, not entirely a layman but also not exactly an expert, if the Photoshop max pixelated entry has the same formula as the detailed comparison it would match? And if that is the case, I imagine all the human input data and behavioral wise would only better the algorithm?
over_clox@lemmy.world 10 hours ago
Looking past the days of old, while also dismissing modern artificial intelligence, the same techniques would still work if you just processed the thumbnails of the images, which for simplicity sake, might as well be a 1/8 scale image, if not actually even lower resolution.