What is it about?
The rise of digitization of cultural documents offers large-scale contents, opening the road for development of AI systems in order to preserve, search, and deliver cultural heritage. To organize such cultural content also means to classify them, a task that is very familiar to modern computer science. Contextual information is often the key to structure such real world data, and we propose to use it in form of a knowledge graph. Such a knowledge graph, combined with content analysis, enhances the notion of proximity between artworks so it improves the performances of art classification.
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Photo by Timon Klauser on Unsplash
Why is it important?
Cultural heritage preserving
Perspectives
This should guide the future work towards automatic art analysis with zero learning.
CHEIKH BRAHIM EL VAIGH
Read the Original
This page is a summary of: GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph, August 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3460426.3463636.
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