Integrating Contextual Knowledge to Visual Features for Fine Art Classification
This work addresses the need for more powerful information retrieval and knowledge discovery tools in the artistic domain, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of limited information in automatic art analysis by constructing ArtGraph, an artistic knowledge graph integrating data from WikiArt and DBpedia, which improves artwork attribute prediction accuracy through contextual knowledge injection into deep learning models.
Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata and textual comments. A knowledge graph that integrates a rich body of information about artworks, artists, painting schools, etc., in a unified structured framework can provide a valuable resource for more powerful information retrieval and knowledge discovery tools in the artistic domain. To this end, this paper presents ArtGraph: an artistic knowledge graph based on WikiArt and DBpedia. The graph, implemented in Neo4j, already provides knowledge discovery capabilities without having to train a learning system. In addition, the embeddings extracted from the graph are used to inject "contextual" knowledge into a deep learning model to improve the accuracy of artwork attribute prediction tasks.