Learning Style Similarity for Searching Infographics
This addresses the need for better search and organization of online infographic design resources, though it appears incremental as it builds on existing computer vision techniques.
The paper tackled the problem of measuring style similarity between infographics by learning a metric from human perception data, finding that color histograms combined with HoG features were most effective for characterizing infographic style.
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.