[JayShower]

Alternative solution that would require less heavy lifting of ML but a little more upfront programming: It sounds like the cars are arranged in a grid on the wall. Maybe it would be possible to narrow down which car the user took a photo of by looking at the photos of the surrounding cars as well, and hardcoding into the system the position of each car relative to one another? Could potentially do that locally very quickly (maybe even at the level of QR-code speed) versus doing an embedding + LLM.

Con of this approach would be that it’s requires maintenance if they ever decide to change the illustration positions.

[armchairhacker]

Put each painting in an artsy frame whose edges are each different, colorful pattern. When the user photographs the painting, they’ll include all (or even most) of the frame, and distinguishing the frames is easy.

[arkh]

> artsy frame

Embedding a QR code or simply a barcode somewhere and you're done. Maybe hide it like a watermark so it does not show to the naked eye and doing some Fourier transform in the app won't require a network connection nor lot of processing power.

[ndileas]

the article does mention that the client rejected a similar approach. steganography seems like a bad choice for a museum setting where you don't own the images.

[jaffa2]

This seems the way to go… its only 350 images