Non-Machine Learning Image Matching with a Vector DB

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • image-algo-testing

    Testing various image matching algorithms' performance on the Pinecone vector DB

  • similarity

    TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

  • There is the metric learning problem to learn a hash for similarity https://github.com/tensorflow/similarity

    That said, I don't see many good models available for download on tfhub or huggingface optimized for it, but you can always programmatically modify your images (if you truly mean identical to humans) - change white balance, crop, rotate, select adjacent frames from videos, etc. and optimize a network that is small enough for you to be satisfied and see if that works, as a possible alternative.

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  • Milvus

    A cloud-native vector database, storage for next generation AI applications

  • Cool to see a vector search application that leverages traditional image features. I'm curious to know which of these methods performed best on cropped images, which remain somewhat of a challenge for traditional classification models (contrastive models trained specifically with image crops tend to work much better).

    Also for the vector database itself, have you considered spinning up your own open-source alternative such as Milvus (https://github.com/milvus-io/milvus), or were you only considering managed services?

  • image-match

    🎇 Quickly search over billions of images

  • 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!

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