# Toward Automated Discovery of Artistic Influence

arXiv: https://arxiv.org/abs/1408.3218 · HTML: https://mlsanity.com/paper/1408.3218/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2014.

## Built on by (most cited first, 8 of 8)

- [ArtSeek: Deep artwork understanding via multimodal in-context reasoning and late interaction retrieval](https://mlsanity.com/paper/2507.21917/) (2025)
- [A deep learning approach to clustering visual arts](https://mlsanity.com/paper/2106.06234/) (2021)
- [Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting](https://mlsanity.com/paper/2107.06935/) (2021)
- [How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network](https://mlsanity.com/paper/2203.16031/) (2022)
- [Inching Towards Automated Understanding of the Meaning of Art: An Application to Computational Analysis of Mondrian's Artwork](https://mlsanity.com/paper/2302.00594/) (2022)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)
- [Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems](https://mlsanity.com/paper/2603.29211/) (2026)
- [M-ArtAgent: Evidence-Based Multimodal Agent for Implicit Art Influence Discovery](https://mlsanity.com/paper/2604.07468/) (2026)

## Abstract (arXiv)

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works