We present VizioMetrix, a platform that extracts visual information from the scientific literature and makes it available for use in new information retrieval applications and for studies that look at patterns of visual information across millions of papers. New ideas are conveyed visually in the scientific literature through figures -diagrams, photos, visualizations, tables -but these visual elements remain ensconced in the surrounding paper and difficult to use directly to facilitate information discovery tasks or longitudinal analytics. Very few applications in information retrieval, academic search, or bibliometrics make direct use of the figures, and none attempt to recognize and exploit the type of figure, which can be used to augment interactions with a large corpus of scholarly literature. The VizioMetrix platform processes a corpus of documents, classifies the figures, organizes the results into a cloudhosted databases, and drives three distinct applications to support bibliometric analysis and information retrieval. The first application supports information retrieval tasks by allowing rapid browsing of classified figures. The second application supports longitudinal analysis of visual patterns in the literature and facilitates data mining of these figures. The third application supports crowdsourced tagging of figures to improve classification, augment search, and facilitate new kinds of analyses. Our initial corpus is the entirety of PubMed Central (PMC), and will be released to the public alongside this paper; we welcome other researchers to make use of these resources.
Lee et al. REF extract images from a large corpus of scientific papers and classify the figure types (e.g., equation, diagram, plot and table).
VizioMetrix: A Platform for Analyzing the Visual Information in Big Scholarly Data
WWW
Computer Science