# Learning Style Similarity for Searching Infographics

arXiv: https://arxiv.org/abs/1505.01214 · HTML: https://mlsanity.com/paper/1505.01214/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2015.

## Built on by (most cited first, 5 of 5)

- [Learning Personal Style from Few Examples](https://mlsanity.com/paper/2105.14457/) (2021)
- [Generalization of CNNs on Relational Reasoning with Bar Charts](https://mlsanity.com/paper/2503.00086/) (2025)
- [Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights](https://mlsanity.com/paper/2505.10043/) (2025)
- [Safire: Similarity Framework for Visualization Retrieval](https://mlsanity.com/paper/2510.16662/) (2025)
- [Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support](https://mlsanity.com/paper/2604.07989/) (2026)

## Abstract (arXiv)

Infographics are complex graphic designs integrating text, images, charts and sketches. Despite the increasing popularity of infographics and the rapid growth of online design portfolios, little research investigates how we can take advantage of these design resources. In this paper we present a method for measuring the style similarity between infographics. Based on human perception data collected from crowdsourced experiments, we use computer vision and machine learning algorithms to learn a style similarity metric for infographic designs. We evaluate different visual features and learning algorithms and find that a combination of color histograms and Histograms-of-Gradients (HoG) features is most effective in characterizing the style of infographics. We demonstrate our similarity metric on a preliminary image retrieval test.