FigureSeer: Parsing Result-Figures in Research Papers
‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent
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Allen Institute for Artificial Intelligence, Seattle, USA [email protected] 2 University of Washington, Seattle, USA http://allenai.org/plato/figureseer
Abstract. ‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search engines, retrieving such information poses a cumbersome challenge today as these systems have primarily focused on understanding the text content of scholarly documents. In this paper, we introduce FigureSeer, an end-to-end framework for parsing result-figures, that enables powerful search and retrieval of results in research papers. Our proposed approach automatically localizes figures from research papers, classifies them, and analyses the content of the result-figures. The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach.
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Computer Vision for Scholarly Big Data
Academic research is flourishing at an unprecedented pace. There are already over 100 million papers on the web [1] and many thousands more are being added every month [2]. It is a Sisyphean ordeal for any single human to cut through this information overload and be abreast of the details of all the important results across all relevant datasets within any given area of research. While academicsearch engines like Google Scholar, CiteSeer, etc., are helping us discover relevant information with more ease, these systems are inherently limited by the fact that their data mining and indexing is restricted to the text content of the papers. Research papers often use figures for reporting quantitative results and analysis, as figures provide an easy means for communicating the key experimental observations [3]. In many cases, the crucial inferences from the figures are often Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46478-7 41) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 664–680, 2016. DOI: 10.1007/978-3-319-46478-7 41
FigureSeer: Parsing Result-Figures in Research Papers
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Fig. 1. FigureSeer is an end-to-end framework for parsing result-figures in research papers. It automatically localizes figures, classifies them, and analyses their content (center). FigureSeer enables detailed indexing, retrieval, and redesign of result-figures, such as highlighting specific results (top-left), reformatting results (bottom-left), complex query answering (top-right), and results summarization (bottom-right).
not explicitly stated in text (as humans can easily deduce them
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