Great article. We used something very similar to help implement simlarity search at Yahoo a couple years back (https://yahooresearch.tumblr.com/post/158115871236/introduci...). We were using a indexing strategy called Locally Optimized Product Quantization, which worked great in terms of query times but required a training procedure which made successive inserts fairly inefficient.
Thankfully, we have a much wider variety of indexing options these days (https://milvus.io/docs/index.md) in addition to powerful vector databases (https://zilliz.com/learn/what-is-vector-database). I'm glad to see the barrier to entry for semantic image retrieval becoming lower and lower as ML infrastructure matures.
Thankfully, we have a much wider variety of indexing options these days (https://milvus.io/docs/index.md) in addition to powerful vector databases (https://zilliz.com/learn/what-is-vector-database). I'm glad to see the barrier to entry for semantic image retrieval becoming lower and lower as ML infrastructure matures.
[EDIT] Disclosure: I work at Zilliz.