# Structure-aware Visualization Retrieval

arXiv: https://arxiv.org/abs/2202.05960 · HTML: https://mlsanity.com/paper/2202.05960/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2022.

## Built on by (most cited first, 11 of 11)

- [Visual correspondence-based explanations improve AI robustness and human-AI team accuracy](https://mlsanity.com/paper/2208.00780/) (2022)
- [FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding](https://mlsanity.com/paper/2407.05183/) (2024)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model](https://mlsanity.com/paper/2304.14630/) (2023)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [ChartKG: A Knowledge-Graph-Based Representation for Chart Images](https://mlsanity.com/paper/2410.09761/) (2024)
- [HaHeAE: Learning Generalisable Joint Representations of Human Hand and Head Movements in Extended Reality](https://mlsanity.com/paper/2410.16430/) (2024)
- [Safire: Similarity Framework for Visualization Retrieval](https://mlsanity.com/paper/2510.16662/) (2025)
- [VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design](https://mlsanity.com/paper/2508.07497/) (2025)
- [Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support](https://mlsanity.com/paper/2604.07989/) (2026)
- [HC-RAG: Evidence-Centric Retrieval-Augmented Generation over Heterogeneous Financial Filings](https://mlsanity.com/paper/2608.12335/) (2026)

## Abstract (arXiv)

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structure-aware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.