# Colors -Messengers of Concepts: Visual Design Mining for Learning Color Semantics

arXiv: https://arxiv.org/abs/1505.06532 · HTML: https://mlsanity.com/paper/1505.06532/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2015.

## Built on by (most cited first, 7 of 7)

- [VINS: Visual Search for Mobile User Interface Design](https://mlsanity.com/paper/2102.05216/) (2021)
- [GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks](https://mlsanity.com/paper/2101.09978/) (2021)
- [GenColor: Generative Color-Concept Association in Visual Design](https://mlsanity.com/paper/2503.03236/) (2025)
- [Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic](https://mlsanity.com/paper/2203.13770/) (2022)
- [Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Apps](https://mlsanity.com/paper/2307.00756/) (2023)
- [Large Language Models estimate fine-grained human color-concept associations](https://mlsanity.com/paper/2406.17781/) (2024)
- [GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity](https://mlsanity.com/paper/2511.17443/) (2025)

## Abstract (arXiv)

This paper studies the concept of color semantics by modeling a dataset of magazine cover designs, evaluating the model via crowdsourcing, and demonstrating several prototypes that facilitate color-related design tasks. We investigate a probabilistic generative modeling framework that expresses semantic concepts as a combination of color and word distributions -color-word topics. We adopt an extension to Latent Dirichlet Allocation (LDA) topic modeling called LDA-dual to infer a set of color-word topics over a corpus of 2,654 magazine covers spanning 71 distinct titles and 12 genres. While LDA models text documents as distributions over word topics, we model magazine covers as distributions over color-word topics. The results of our crowdsourced experiments confirm that the model is able to successfully discover the associations between colors and linguistic concepts. Finally, we demonstrate several simple prototypes that apply the learned model to color palette recommendation, design example retrieval, image retrieval, image color selection, and image recoloring.