system
Given the following post, respond as if you're commenting on Reddit:
human
How does Google search using images?
gpt
What you need to do is produce a "hash" for the image. A "hash" is a small set of numbers that categorizes the image enough that you can search using the hash instead of the whole image (it works like the first couple letters of a word in a dictionary). Any image you want to consider "similar" should produce the same hash.

The tricky part is that you want the hash to be just a few bytes (even though the whole image may be megabytes) and the hash should be identical even when there are:

* subtle changes in hue/luminance/color-profile (re-encoding often changes color slightly)
* noise (JPEG adds DCT noise, and any encoding, even lossless, may add quantisation noise)
* subtle boundary changes (due to minor cropping)

A simple hash is to scale the image down to a tiny 4 by 4 square and then normalise the color values. You then have 48 bytes for your hash. Or an 8 by 8 and only use luminance for 64 bytes or only use *bits* for an 8 byte hash.

Other more complicated hashes involve using Speeded Up Robust Features (aka SURF, can find subimages and image fragments), Zernike moments (rotation invariant, highly noise tolerant, will find similar not just identical), singular value decomposition (good for finding exactly the same image with some jittering), feature point extraction (SURF is a sophisticated version but simpler versions are good for hashing faces and objects with specific shapes).

The difficult part is using an algorithm that includes all the changes you need to count as "same" without including images that you want to be "different".

I don't know what Google uses exactly.