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bubudumbdumb t1_j33xh4d wrote

Sift is good if you want to match images of the same building or cereal box seen from another point of view or with different lightning.

If you want to match images that have dogs or cars or Bavarian houses you might need some sort of convolutional auto encoder as a featuriser.

If you have a lot of GPUs available you can use ViT, a transformer based architecture, to compute features.

Once you have features you might use a nearest neighbors library to find close representations.

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SCP_radiantpoison t1_j33ya2o wrote

What if you wanted to match faces? OpenCV has a NN module that detects faces, is there a good solution for face recognition against a database?

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bubudumbdumb t1_j34fk6v wrote

In the last month I came across a blog post about vector databases. The post argued that there are a few basic types of distances (L1, L2, cosine) and that you are going to have better fortune using a vector database that supports those than searching using your own heuristic and hybrid solutions. So my suggestion would be to represent faces in some space that you can search over with a vector database or with some nearest neighbors index

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