# Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors

arXiv: https://arxiv.org/abs/2009.03807 · HTML: https://mlsanity.com/paper/2009.03807/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Semi-supervised Human Pose Estimation in Art-historical Images](https://mlsanity.com/paper/2207.02976/) (2022)
- [Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History](https://mlsanity.com/paper/2301.05124/) (2023)
- [Automatic Analysis of Human Body Representations in Western Art](https://mlsanity.com/paper/2210.08860/) (2022)
- [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset](https://mlsanity.com/paper/2507.08384/) (2025)
- [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)
- [ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas](https://mlsanity.com/paper/2206.11115/) (2022)
- [Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods](https://mlsanity.com/paper/2410.15866/) (2024)
- [Reading Legends on Ancient Coins: An Object Detection Approach for Character Recognition on a Novel Roman Republican Dataset](https://mlsanity.com/paper/2607.25455/) (2026)

## Abstract (arXiv)

Image compositions as a tool for analysis of artworks is of extreme significance for art historians. These compositions are useful in analyzing the interactions in an image to study artists and their artworks. Max Imdahl in his work called Ikonik, along with other prominent art historians of the 20th century, underlined the aesthetic and semantic importance of the structural composition of an image. Understanding underlying compositional structures within images is challenging and a time consuming task. Generating these structures automatically using computer vision techniques (1) can help art historians towards their sophisticated analysis by saving lot of time; providing an overview and access to huge image repositories and (2) also provide an important step towards an understanding of man made imagery by machines. In this work, we attempt to automate this process using the existing state of the art machine learning techniques, without involving any form of training. Our approach, inspired by Max Imdahl's pioneering work, focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background. Currently, our approach works for artworks comprising of protagonists (persons) in an image. In order to validate our approach qualitatively and quantitatively, we conduct a user study involving experts and non-experts. The outcome of the study highly correlates with our approach and also demonstrates its domain-agnostic capability. We have open-sourced the code at https://github.com/image-compostion-canvas-group/image-compostion-canvas.