VIStory: interactive storyboard for exploring visual information in scientific publications

Abstract

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present VIStory, an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/ height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings-a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of VIStory with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of VIStory in exploring visual information in scientific publications. Keywords Document visualization Á Image browser Á Faceted metadata 1 Introduction Publications are one of the most important outcomes of scientific research. Together with the development of science itself, substantial amounts of scientific publications have been generated. Though digital libraries like Google Scholar and Microsoft Academic provide powerful searching and browsing functionalities, they are often found ineffective for high-level tasks such as collaboration analysis. Visual analytics has gained intense interest in exploring scientific publications, as it can enable human cognition and reasoning with machine's powerful computing capacity (Keim et al. 2008). Vast amounts of visual analytics have been developed that facilitate applications including literature review and citation analysis (e.g.

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