[jasonmar]

See this example for how neural network can be used to "sort and search by vague similarity and classify on a spectrum of multiple qualities". youtu.be/5PNnPagENxQ?t=1540

Descartes Labs uses the pre-trained ResNet 50 and removes the final layer which does classification. What's left is a layer that provides image features necessary to do classification. These features can be used to sort images by similarity and search for similar images.

[joe_the_user]

Indeed. Good and cool but I still claim this is not quite "it". I may not have specified "it" fully but feel I like, "it", meaning, can be intuitively obvious.

They get a vector of approximate features and can use it match to other images.

BUT there's still the "this means nothing" problem. The vectors, as far I can tell and by the logic of just doing autoencoding, don't have a significance except for the system. Can find image X and say it's like image Y.

But it doesn't help at all at finding specified things. You can't say "find me a corn field" or "find my nuclear power plant". You can show it a picture of nuclear power plant and it can show you mountains with a similar layout.