💡When designing an image API, it’s worth going beyond just returning image URLs. 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
Including image features in the JSON response lowers technical entry barriers and enables powerful applications – not just for data scientists, but also for curators, scholars, and developers working on visual interfaces. #metaimage #bigimagedata
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! #bigvisualdata #culturaldata