GCNBoost: Artwork Classification by Label Propagation through a
Knowledge Graph
- URL: http://arxiv.org/abs/2105.11852v1
- Date: Tue, 25 May 2021 11:50:05 GMT
- Title: GCNBoost: Artwork Classification by Label Propagation through a
Knowledge Graph
- Authors: Cheikh Brahim El Vaigh, Noa Garcia, Benjamin Renoust, Chenhui Chu,
Yuta Nakashima and Hajime Nagahara
- Abstract summary: Contextual information is often the key to structure such real world data, and we propose to use it in form of a knowledge graph.
We propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data.
With label propagation, we boost artwork classification by training a model using a graph convolutional network.
- Score: 32.129005474301735
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 in classification tasks. In this
paper, we propose a novel use of a knowledge graph, that is constructed on
annotated data and pseudo-labeled data. With label propagation, we boost
artwork classification by training a model using a graph convolutional network,
relying on the relationships between entities of the knowledge graph. Following
a transductive learning framework, our experiments show that relying on a
knowledge graph modeling the relations between labeled data and unlabeled data
allows to achieve state-of-the-art results on multiple classification tasks on
a dataset of paintings, and on a dataset of Buddha statues. Additionally, we
show state-of-the-art results for the difficult case of dealing with unbalanced
data, with the limitation of disregarding classes with extremely low degrees in
the knowledge graph.
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