demonstration

Autotator: Semi-Automatic Approach for Accelerating the Chart Image Annotation Process

Authors:

Junhoe Kim,

Jaemin Jo,

Jinwook SeoAuthors Info & Claims

ISS '19: Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces

Pages 315 - 318

https://doi.org/10.1145/3343055.3360741

Published: 10 November 2019 Publication History

Get Access

Abstract

Annotating chart images for training machine learning models is tedious and repetitive especially in that chart images often have a large number of visual elements to annotate. We present Autotator, a semi-automatic chart annotation system that automatically provides suggestions for three annotation tasks such as labeling a chart type, annotating bounding boxes, and associating a quantity. We also present a web-based interface that allows users to interact with the suggestions provided by the system. Finally, we demonstrate a use case of our system where an annotator builds a training corpus of bar charts.

Supplementary Material

MP4 File (issde06.mp4)

Supplemental video

Download
40.00 MB

References

[1]

Abhijit Balaji, Thuvaarakkesh Ramanathan, andVenkateshwarlu Sonathi. 2018. Chart-Text: A FullyAutomated Chart Image Descriptor.arXiv preprintarXiv:1812.10636(2018).

Google Scholar

[2]

Aaron Baucom and Christopher Echanique. 2013.ScatterScanner: Data extraction and chart restyling ofscatterplots. (2013).

Google Scholar

[3]

Jinho Choi, Sanghun Jung, Deok Gun Park, JaegulChoo, and Niklas Elmqvist. 2019. Visualizing for theNon-Visual: Enabling the Visually Impaired to UseVisualization.Computer Graphics Forum38, 3 (2019),249--260.

Crossref

Google Scholar

[4]

Daniel Haehn, James Tompkin, and Hanspeter Pfister.2018. Evaluating Graphical Perception with CNNs.IEEE transactions on visualization and computergraphics25, 1 (2018), 641--650.

Crossref

Google Scholar

[5]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and JianSun. 2016. Deep residual learning for imagerecognition. InProceedings of the IEEE conference oncomputer vision and pattern recognition. 770--778.

Google Scholar

[6]

Daekyoung Jung, Wonjae Kim, Hyunjoo Song,Jeong-in Hwang, Bongshin Lee, Bohyoung Kim, andJinwook Seo. 2017. Chartsense: Interactive dataextraction from chart images. InProceedings of the2017 CHI Conference on Human Factors in ComputingSystems. ACM, 6706--6717.

Google Scholar

[7]

Samira Ebrahimi Kahou, Vincent Michalski, AdamAtkinson, Ákos Kádár, Adam Trischler, and YoshuaBengio. 2017. Figureqa: An annotated figure datasetfor visual reasoning.arXiv preprint arXiv:1710.07300(2017).

Google Scholar

[8]

Diederik P Kingma and Jimmy Ba. 2014. Adam: Amethod for stochastic optimization.arXiv preprintarXiv:1412.6980(2014).

Google Scholar

[9]

Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He,Bharath Hariharan, and Serge Belongie. 2017. Featurepyramid networks for object detection. InProceedingsof the IEEE conference on computer vision and patternrecognition. 2117--2125.

Google Scholar

[10]

Jorge Poco and Jeffrey Heer. 2017.Reverse-Engineering Visualizations: RecoveringVisual Encodings from Chart Images. InComputerGraphics Forum, Vol. 36. Wiley Online Library,353--363.

Google Scholar

[11]

Jorge Poco, Angela Mayhua, and Jeffrey Heer. 2017.Extracting and retargeting color mappings from bitmapimages of visualizations.IEEE transactions onvisualization and computer graphics24, 1 (2017),637--646.

Google Scholar

[12]

Manolis Savva, Nicholas Kong, Arti Chhajta, Li Fei-Fei,Maneesh Agrawala, and Jeffrey Heer. 2011. Revision:Automated classification, analysis and redesign ofchart images. InProceedings of the 24th annual ACMsymposium on User interface software and technology.ACM, 393--402.

Google Scholar

[13]

Noah Siegel, Zachary Horvitz, Roie Levin, SantoshDivvala, and Ali Farhadi. 2016. FigureSeer: Parsingresult-figures in research papers. InEuropeanConference on Computer Vision. Springer, 664--680.

Google Scholar

Cited By

View all

Bromley DSetlur V(2023)What Is the Difference Between a Mountain and a Molehill? Quantifying Semantic Labeling of Visual Features in Line Charts2023 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS54172.2023.00041(161-165)Online publication date: 21-Oct-2023
https://doi.org/10.1109/VIS54172.2023.00041

Index Terms

Autotator: Semi-Automatic Approach for Accelerating the Chart Image Annotation Process
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
    2. Interactive systems and tools
      1. User interface toolkits
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social tagging systems

Recommendations

Semi-automatic semantic annotation of PubMed queries

Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a ...
Automatic gazette creation for named entity recognition and application to resume processing
COMPUTE '12: Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies

Named entities are important content-carrying units within documents. Consequently named entity recognition (NER) is an important part of information extraction. One fast and accurate approach to NER uses a list or gazette consisting of known instances. ...
Two-stage approach to named entity recognition using Wikipedia and DBpedia
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

In natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...

Comments

Information & Contributors

Information

Published In

ISS '19: Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces

November 2019

450 pages

ISBN:9781450368919

DOI:10.1145/3343055

General Chairs:
Bongshin Lee
Microsoft Research, USA
,
Geehyuk Lee
KAIST, South Korea
,
Program Chairs:
Stacey Scott
University of Guelph, Canada
,
Melanie Tory
Tableau Software, USA
,
Jeonghyun Kim
Korea University, South Korea

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2019

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

ISS '19

Sponsor:

SIGCHI

ISS '19: Interactive Surfaces and Spaces

November 10 - 13, 2019

Daejeon, Republic of Korea

Acceptance Rates

ISS '19 Paper Acceptance Rate 26 of 85 submissions, 31%;

Overall Acceptance Rate 147 of 533 submissions, 28%

Upcoming Conference

ISS '24

Sponsor:
sigchi

Conference on Interactive Surfaces and Spaces

October 27 - 30, 2024

Vancouver , BC , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
191
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)3

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bromley DSetlur V(2023)What Is the Difference Between a Mountain and a Molehill? Quantifying Semantic Labeling of Visual Features in Line Charts2023 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS54172.2023.00041(161-165)Online publication date: 21-Oct-2023
https://doi.org/10.1109/VIS54172.2023.00041

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Semi-automatic semantic annotation of PubMed queries

Automatic gazette creation for named entity recognition and application to resume processing

Two-stage approach to named entity recognition using Wikipedia and DBpedia