# An Image-based Typology for Visualization

arXiv: https://arxiv.org/abs/2403.05594 · HTML: https://mlsanity.com/paper/2403.05594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 170 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository](https://mlsanity.com/paper/1905.04616/) (2019)
- [Beagle: Automated Extraction and Interpretation of Visualizations from the Web](https://mlsanity.com/paper/1711.05962/) (2017)
- [Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts](https://mlsanity.com/paper/2208.01780/) (2022)
- [VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications](https://mlsanity.com/paper/2101.01036/) (2020)
- [Direct Volume Rendering with Nonparametric Models of Uncertainty](https://mlsanity.com/paper/2008.13576/) (2020)
- [Topology Density Map for Urban Data Visualization and Analysis](https://mlsanity.com/paper/2007.15828/) (2020)
- [Toward Quantifying Ambiguities in Artistic Images](https://mlsanity.com/paper/2008.09688/) (2020)
- [VAID: Indexing View Designs in Visual Analytics System](https://mlsanity.com/paper/2211.02567/) (2022)
- [Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis](https://mlsanity.com/paper/2009.03115/) (2020)
- [Implicit Multidimensional Projection of Local Subspaces](https://mlsanity.com/paper/2009.03259/) (2020)
- [Cartographic Relief Shading with Neural Networks](https://mlsanity.com/paper/2010.01256/) (2020)
- [Toward a deeper understanding of Visualization through keyword analysis](https://mlsanity.com/paper/1408.3297/) (2014)
- [Scalability of Network Visualisation from a Cognitive Load Perspective](https://mlsanity.com/paper/2008.07944/) (2020)
- [Affective Visualization Design: Leveraging the Emotional Impact of Data](https://mlsanity.com/paper/2308.02831/) (2023)
- [Toward A Deep Understanding of What Makes a Scientific Visualization Memorable](https://mlsanity.com/paper/1808.00607/) (2018)
- [Sea of Genes: Combining Animation and Narrative Strategies to Visualize Metagenomic Data for Museums](https://mlsanity.com/paper/1906.01071/) (2019)
- [SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization](https://mlsanity.com/paper/2007.15591/) (2020)
- [BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations](https://mlsanity.com/paper/2207.14147/) (2022)
- [Data Visceralization: Enabling Deeper Understanding of Data Using Virtual Reality](https://mlsanity.com/paper/2009.00059/) (2020)
- [Subjective visualization experiences: impact of visual design and experimental design](https://mlsanity.com/paper/2310.13713/) (2023)

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

- [Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization](https://mlsanity.com/paper/2509.05718/) (2025)

## Abstract (arXiv)

We present and discuss the results of a qualitative analysis of visualization images to derive an image-based typology of visualizations. For each image, we seek to identify its main focus or the essential stimuli. As a result, we derived 10 image-based visualization types. We describe coding decisions we made in the derivation process. The resulting image typology can serve a number of purposes: enabling researchers and practitioners to identify visual design styles, facilitating the categorization of visualization images for the purpose of research and teaching, enabling researchers to study the evolution of the community and its research output over time, and facilitating a discussion of standardization in visualization. In addition, the tool and dataset enable scholars to closely examine the images and how they are published and communicated in our community. osf.io/dxjwt presents a pre-registration and all supplemental materials.