image-match

I also developed a service based on the Goldberg paper in the mid 2010s. We flattened and discretized the signature to make it searchable which gave us pretty good results: https://github.com/ProvenanceLabs/image-match Sorry I haven’t maintained the project in years so it’s unlikely to work out of the box. But who knows, maybe you’ll find something useful here for your project!

Another open-source implementation using a similar concept and Elasticsearch is image-match: https://github.com/ascribe/image-match

Hi everyone, I'm the author of the underlying image matching library ( https://github.com/ascribe/image-match ). First, thank you Alex for your contribution -- it makes image-match much more useful for the typical user. Just to answer a couple of questions in the comments: Goldberg's algorithm (the one used in the image-match library) is not very robust against arbitrary rotation -- around +/- 5 degrees should be ok. 90 degree rotations, mirror images, and color inversion are handled with the `all_orientations` parameter but under the hood this is just manipulating the image array and searching multiple times. Even though the hash is a vector, and the similarity is a vector distance, when it's time to search for an image, we don't compute every distance. The hash vector is binned into integer "words" and we lookup against these columns

Uhh... that's a fork. This is the original repo: https://github.com/rhsimplex/image-match