Exploring Visual Information Flows in Infographics

1. Exploring Visual Information Flows in Infographics
CHI 2020 Paper
Min Lu, Chufeng Wang, Joel Lanir, Nanxuan Zhao, Hanspeter Pfister, Daniel Cohen-Or and Hui Huang
Presenter: Hwiyeon Kim

2. Overview of the paper
• Motivation of the study
 Infographics
• have a variety of design poses
 Challenges
• Infographics are composed of various visual
elements with diverse appearances
• Spatial arrangement of the visual elements are
diverse
 do not necessarily follow a well-known
structure
• Goal of the study
 Understanding the design space and common
patterns of information flow
 Supporting better infographics design for
novices

3. Background Knowledge
• Infographics
 Visual representations consisting of graphical
elements and data components designed to convey
an informative narrative
 Combining visual elements + text  organic story is
essential to convey message
• Static graphical elements
• Text
• Notable embellishments
3

4. Background Knowledge
• Making analysis of infographics’ design space difficult
1. Infographics are composed of various visual elements (e.g.,
icons, images, embellishments, or text)
• Designers usually create them with an aesthetic and creative
mindset
• Often injecting them with personality and style
2. The spatial arrangement of these visual elements is carefully
chosen to convey a unique idea to the audience
• Arrangements are generally diverse
• Do not necessarily follow a well-known structure
3

5. 4
Background Knowledge
• A concept of Visual Information Flow (VIF)
 the underlying semantic structure that links th
e graphical data elements to convey the inform
ation and story to the user
 to understand visual organization of stories.

6. Background Knowledge
• Track and study users’ reactions to infographics
 Visual embellishments (Bateman et al. 2010, Haroz et al. 2015)
 Attention (Kim et al. 2017)
 Memorability (Borkin et al. 2013)
 First impression (Haroz et al. 2015)
3
The goal of Exploration of Visual Information Flow (VIF):
Learn the design patterns from the perspective of infographics creators rather than end users

7. Background Knowledge
• Visual Information Organization
 Storytelling (Bradley et al. 2015, Brechman et al. 2010)
 Design patterns of organizing visual information
• seven distinct genres of narrative visualization (Segel and Heer, 2010)
• design of timeline in storytelling (Brehmer et al. 2017)
• design-patterns for rapid storyboarding of data comics (Hullman et al., 2013)
 Visual organization of information and improve visual expressiveness in storytelling
• method for designing graphical elements enhanced with data (Kim et al., 2016)
• systematic framework for augmenting graphics with data (Liu et al., 2018)
• visual design tool for easily creating design-driven infographics (Wang et al., 2018)
• Build visualization scenes that include annotations in order to tell a story (Satyanarayan and Heer., 2014)
3
This paper’s work explores visual information flows in static infographics without user interactions,
from which the distilled design patterns can empower the design of infographics authoring tools.

8. Contribution of this paper
• A method
 for the breakdown of infographics and the construction of its VIF (Visual Information Flow) from automa
tically detected elements
• A taxonomy
 of main VIF design patterns and exploration of the VIF design space
• A system
 that supports infographic search according to VIF patterns
• A large dataset
 of 13,245 infographic templates
 4300 include annotated boundary boxes of elements
 a dataset of 965 infographics with real data
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9. 8
Method for automatic construction and analysis of VIF in infographics
• Composition
 Artistic elements – make the infographic vis
ually appealing
 Graphical data – convey the information

10. 9
Method for automatic construction and analysis of VIF in infographics
VIF Construction Pipeline
Data Element Extraction
locate the visual data elements related to the vis
ual information flow

11. 10
Method for automatic construction and analysis of VIF in infographics
VIF Construction Pipeline
Data Element Extraction
locate the visual data elements related to the vis
ual information flow
Information Flow Construction
constructed flows in those trials are scored ac
cording to their regularity and the best one is
picked as the visual information flow
b) associate elements
into visual groups
a) trace various information fl
ows among the visual groups

12. 11
Method for automatic construction and analysis of VIF in infographics
VIF Construction Pipeline
Data Element Extraction
locate the visual data elements related to the vis
ual information flow
Information Flow Construction
constructed flows in those trials are scored ac
cording to their regularity and the best one is
picked as the visual information flow
b) associate elements
into visual groups
a) trace various information fl
ows among the visual groups

13. 12
Method for automatic construction and analysis of VIF in infographics
InfoVIF Dataset
With the keyword ‘infographics’ in Freepik and Shutterstock
1. The infographics composed of multiple subfigures
2. Only with figures with texts
InfoVIF (http://47.103.22.185:8089/ now unavailable..)
13,245 infographic (68% from Freepick / 32% from Shutterstock)

14. 13
Method for automatic construction and analysis of VIF in infographics
Data Element Extraction
Detach graphical data elements from artistic decorations
YOLO (Redmon et al., 2016)
• Deep Neural Networks
• object detection by learning from large scale human-labeled dat
asets
• To extract data elements
• Text (Title / Body text)
• Icon
• Index (1~9, 01~09, A~G)
• Arrow (left, left-top, top…)
 36 labels for graphical data elements in total
• For overfitting problems
1. Convert images to gray scale
2. Cropping
25k annotated training images

15. 14
Method for automatic construction and analysis of VIF in infographics
Information Flow Construction i. Select seeds
a) Index Priority
• prioritize elements that carry some semantics that sug
gest an indexing order (e.g., text or icons get lower pri
ority)
b) Shape similarity
• detected elements with same tag or shape are conside
red allies of the seed
ii. Trace flow
a) Shortest Path
• designers consider information flow in the shorter dist
ances
b) Regularity
• evaluate regularity by the std of (line segment lengths,
adjacent horizontal shifts, adjacent vertical shifts, turni
ng angles)
c) Common Reading Order
• take the most common reading order to decide the flo
w if no explicit hints exist (e.g., top  bottom, left  r
ight, clockwise)

16. 15
Method for automatic construction and analysis of VIF in infographics
Information Flow Construction i. Select seeds
ii. Trace flow
iii. Compose visual groups
a) Elements in Proximity
a) Similarity among Groups - measure the similarit
y between visual group i and j with the Jaccard
coefficient:

17. 16
Method for automatic construction and analysis of VIF in infographics
Design Pattern Exploration
• Inspired by the iterative method of Segel and Heer (2010) for ex
ploration of narrative visualization designs
• Semi-automated technique to extract the VIF design patterns fro
m their massive dataset
Basic procedure
i. the construction of a comprehensive VIF design space and initi
alization of possible design patterns
• Principal Component Analysis (PCA) to extract the top 50 principal
components
• VIF signatures were projected to a 2D space using t-SNE
ii. multiple iterations to construct a categorization of the design p
atterns.

18. 17
VIF Taxonomy
VIF patterns design dimensions
• Backbone shape
• Circular information flows
• Linear information flows
• Content placement
• Circular
• Clock – placed inside or
on top of the backbone
• Star – spread outside the
center
• Horizontal
• Landscape / Portrait
• Pulse / Spiral
• Asymmetrical balance
• Up-ladder / Down-ladder

19. 18
VIF Taxonomy

20. 19
My criticism for this paper
• Gifts for infoVIS researchers
• Pointed out challenge point of infographics analysis and covered
through various method
• Collected and shared a lot of infographics data (not available now)
and shared their analysis results and tools as open source
• Computer scientists’ eye on Design
• Always interesting to see how computer science researchers try to
analyze design results
• Most of them want to distill design patterns and evaluate design in
quantitative
• Education
• Applicable enough for infographics job training for junior designers
• Researchers should keep in mind that professional designers do no
t prefer new authoring tools, because they already have their main
tools (e.g., Adobe illustrator) and design style (don’t forget this wh
en you’re selecting target groups in your research)

21. My criticism for this paper
20
• Limitations of design pattern analysis
• Need to collect a huge amount of
reliable design data
• Cannot cover ALL OF THEM (which is
disappointing)
• Design trends rise and fall very quickly
• Maybe it’s hard to expect future trend
• A field where creative and new things
emerge competitively
• Lessons Learned
• Can’t access any websites (open data
sources / tools) suggested by authors..
• It is important to keep / achieve your
supplementary materials after
submitting your paper..

22. IDEAs
21
• Theme image analysis
• In our classification of visualization thumbnails , it is classified as
GNRD (Graphics Not Related to Data)
• Type – illustrations, photographs
• Infographics – designed illustrations, maybe it is
• VisThumbnails – photographs, maybe usually articles are related to people, or
because of their time for producing articles
• preferred or typically used images
• Light Bulb infographic?
• Infographics can be organized by topics