VIS30K : A Collection of Figures and Tables From IEEE Visualization Conference Publications
We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
IEEE transactions on visualization and computer graphics - 27(2021), 9 vom: 02. Sept., Seite 3826-3833 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Jian [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 30.07.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TVCG.2021.3054916 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320647730 |
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| 520 | |a We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Ling, Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Isenberg, Petra |e verfasserin |4 aut | |
| 700 | 1 | |a Isenberg, Tobias |e verfasserin |4 aut | |
| 700 | 1 | |a Sedlmair, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Moller, Torsten |e verfasserin |4 aut | |
| 700 | 1 | |a Laramee, Robert S |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Han-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Wunsche, Katharina |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Qiru |e verfasserin |4 aut | |
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