: Infographics combine text, charts and images. We consider the problem of learning style similarity for infographics to enable stylebased search across a repository of infographics. For a query infographic (left), our approach returns the most stylistically similar infographics. This figure shows three example queries, one in each row. 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.
REF All of these applications show that HOG feature is invariant to the illumination.
Learning Style Similarity for Searching Infographics
ArXiv
ArXiv
Computer Science