  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.
