💡When designing an image API, it’s worth going beyond just returning image URLs and the usual meta data.
Including image feature vectors in the response can significantly expand what’s possible for users and researchers. 🧵
#bigimagedata #metaimage #digitalarthistory
Why does it matter?
If the API already provides features (e.g. extracted via ResNet or similar), users can directly perform t-SNE or other dimensionality reduction techniques without first downloading the images and processing them locally.
#bigimagedata #machinelearning
This means: real-time visualization of image similarity becomes feasible, especially for interfaces or dashboards that display clustered search results or artwork collections.
Think search results as semantic maps instead of grids.
#digitalarthistory #AI
I wanted to share this thought with you.
What do you think – especially in the context of visualizing museum and collection data?
Does something like this already exist? What’s considered best practice? Who’s working on this?
Would love to hear your thoughts! 👇
#digitalarthistory #bigvisualdata #culturaldata #museumtech
Harald Klinke