# Computational identification of significant actors in paintings through symbols and attributes

arXiv: https://arxiv.org/abs/2102.02732 · HTML: https://mlsanity.com/paper/2102.02732/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion](https://mlsanity.com/paper/2111.12602/) (2021)
- [Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks](https://mlsanity.com/paper/2203.07026/) (2022)
- [For a semiotic AI: Bridging computer vision and visual semiotics for computational observation of large scale facial image archives](https://mlsanity.com/paper/2407.03268/) (2024)

## Abstract (arXiv)

The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs. The most important difference is that many realist paintings depict stories or episodes in order to convey a lesson, moral, or meaning. One early step in automatic interpretation and extraction of meaning in artworks is the identifications of figures (actors). In Christian art, specifically, one must identify the actors in order to identify the Biblical episode or story depicted, an important step in understanding the artwork. We designed an automatic system based on deep convolutional neural networks and simple knowledge database to identify saints throughout six centuries of Christian art based in large part upon saints symbols or attributes. Our work represents initial steps in the broad task of automatic semantic interpretation of messages and meaning in fine art.