Learning Style Similarity for Searching Infographics

TLDR

Infographics combine text, images, charts, and sketches, yet research on leveraging these designs remains scarce. The study proposes a method to quantify style similarity among infographics. Using crowdsourced perception data, computer vision, and machine learning, the authors learn a style similarity metric for infographics. Color histograms combined with HoG features best capture infographic style, and the metric was validated in a preliminary image retrieval test.

Abstract

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.