Proceedings: GI 2015

Learning style similarity for searching infographics

Babak Saleh , Mira Dontcheva , Aaron Hertzmann , Zhicheng Liu

Proceedings of Graphics Interface 2015: Halifax, Nova Scotia, Canada, 3 - 5 June 2015, 59-64

DOI 10.20380/GI2015.08

  • Bibtex

    @inproceedings{Saleh:2015:10.20380/GI2015.08,
    author = {Saleh, Babak and Dontcheva, Mira and Hertzmann, Aaron and Liu, Zhicheng},
    title = {Learning style similarity for searching infographics},
    booktitle = {Proceedings of Graphics Interface 2015},
    series = {GI 2015},
    year = {2015},
    issn = {0713-5424},
    isbn = {978-1-4822-6003-8},
    location = {Halifax, Nova Scotia, Canada},
    pages = {59--64},
    numpages = {6},
    doi = {10.20380/GI2015.08},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

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.