Color Recommendation for Vector Graphic Documents based on Multi-Palette Representation

TL;DR

This paper introduces a novel multi-palette color recommendation system for vector graphic documents, extracting and combining multiple color palettes per visual element to improve color selection accuracy and designer satisfaction.

Contribution

It proposes a masked color model for sequence completion and a multi-palette approach, outperforming existing methods in color prediction and recommendation quality.

Findings

Outperforms state-of-the-art color prediction methods

Receives positive feedback from professional designers

Achieves high accuracy in color sequence completion

Abstract

Vector graphic documents present multiple visual elements, such as images, shapes, and texts. Choosing appropriate colors for multiple visual elements is a difficult but crucial task for both amateurs and professional designers. Instead of creating a single color palette for all elements, we extract multiple color palettes from each visual element in a graphic document, and then combine them into a color sequence. We propose a masked color model for color sequence completion and recommend the specified colors based on color context in multi-palette with high probability. We train the model and build a color recommendation system on a large-scale dataset of vector graphic documents. The proposed color recommendation method outperformed other state-of-the-art methods by both quantitative and qualitative evaluations on color prediction and our color recommendation system received positive feedback from professional designers in an interview study.