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Angry Crowds: Detecting Violent Events in Videos Read chapter Read first chapter

2016 | OriginalPaper | Chapter

FigureSeer: Parsing Result-Figures in Research Papers

Authors : Noah Siegel, Zachary Horvitz, Roie Levin, Santosh Divvala, Ali Farhadi

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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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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Appendix
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Metadata
Title
FigureSeer: Parsing Result-Figures in Research Papers
Authors
Noah Siegel
Zachary Horvitz
Roie Levin
Santosh Divvala
Ali Farhadi
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46477-0

Electronic ISBN: 978-3-319-46478-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46478-7_41

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