# Integrating Contextual Knowledge to Visual Features for Fine Art Classification

arXiv: https://arxiv.org/abs/2105.15028 · HTML: https://mlsanity.com/paper/2105.15028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph](https://mlsanity.com/paper/2105.11852/) (2021)

## Built on by (most cited first, 1 of 1)

- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)

## Abstract (arXiv)

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.