MK&G Neo Lab
Data Exploration Sprint
non-deterministic data exploration
visual search
unflattening metadata
find object by feeling
generous
interfaces / queries
April 2023
☕️ Igor Rjabinin ☕️ Michal Čudrnák ☕️ Philo van Kemenade ☕️
where to start?
personalised collections
multimodal
similarity
clustering interests
Objects of the month
spring
views
looking for photographs
3210 records
filered for iconography containing "blumen"
blumen.csv
images_blumen/
proof of concept (flask)
Application Programming Interface (API)
def cosine_similarity(vecA, vecB):
return np.dot(vecA,vecB)/(norm(vecA)*norm(vecB))
(private) repo on GitHub
Choose Your Own Object
functional prototype for visitors
start with a curated object,
walk through the collection
along 2 axes (visual & metadata)
tested in gallery, positive responses
Takeaways
exploration benefits from an queryable API 🤖
image embeddings can support intuitive ways
of search, exploration and recommendation 👍
metadata and content (e.g. image) features can be combined into meaningful UX 🤗
working with image embeddings is easier than I thought 😌
Data Sprinting is an interesting format
for insight gathering and rapid prototyping