InfoColorizer

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

This repository contains code and study materials for the paper InfoColorizer: Interactive Recommendation of Color Palettes for Infographics, contributed by Lin-Ping Yuan, Ziqi Zhou, Jian Zhao, Yiqiu Guo, Fan Du, and Huamin Qu. The paper can be found here. All rights reserved by authors.


Introduction

InfoColorizer is a tool that allows users to effectively obtain high-quality color palettes during infographics creation. It consists of a recommendation engine and a visual interface. From a large infographic dataset, we extract a set of features that embeds both structure and color information of infographics (the red arrow). We then train a deep learning model, VAEAC, that characterizes good color design practices in the data, to construct our recommendation engine. With the visual interface, users can obtain recommended color palettes, specify various color preferences and constraints, preview and edit infographics, and retrieve new recommendations in an iterative manner (the blue arrows).

The workflow of InfoColorizer.

Supplementary Materials

Feature Extraction Algorithm

As described in Section 5.1.1, we extract features at different levels and construct a tree that captures spatial relationship of infographic elements. The corresponding code is mainly at ./backend/treeconstructor.

Deployment

Prerequisite:

Node and Python

Environment

- cd frontend
- npm install
- npm run serve

Start the server

- cd backend
- pip install -r requirements.txt (suggest using virtual environment https://docs.python.org/3/tutorial/venv.html)
- bash run-data-backend.sh

Cite this work

@article{yuan2021infocolorizer,
  title={InfoColorizer: Interactive Recommendation of Color Palettes for Infographics},
  author={Yuan, Lin-Ping and Zhou, Ziqi and Zhao, Jian and Guo, Yiqiu and Du, Fan and Qu, Huamin},
  journal={arXiv preprint arXiv:2102.02041},
  year={2021}
}