Content-Based Image Retrieval
pinecone.io
pinecone.io
I clearly remember a tool like that that was not on the desktop, from years and years ago. It was working quite well I'd say. For example you could draw a very crude sun and a blue sky (a simple example) and you'd get matching pictures. Now... It may just have been someone who wrapped ImgSeek and put it as a webapp: no clue. However I'm 100% positive I used an online tool exactly like what you're describing.
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.
Full disclosure: I work for Pinecone. It's important to disclose you work for a company if you're going to promote their links.
On the topic of vector search, Milvus is another great vector database - it's open source and we provide single-line startup scripts via `docker-compose` in addition to installation via apt & yum (https://milvus.io/docs/install_standalone-docker.md). There are also no restrictions on the number of vectors that users can store. Internally, we've successfully scaled Milvus to handle billion+ vectors, while many of our users have stored hundreds of millions of vectors in a production environments as well.
CLIP also does caption embeddings, so you can lookup images via both images and captions.
I wrote a naive, minimal dependency Python package to calculate image embeddings (https://github.com/minimaxir/imgbeddings) with some lookup demo notebooks and it works well in a pinch, although it's due for an upgrade.