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Fritz Lekschas @flekschas
Harvard University, School of Engineering
Michael Behrisch, Benjamin Bach, Peter Kerpedjiev,
Nils Gehlenborg, and Hanspeter Pfister
IEEE VIS
Oct 23rd 2019
PATTERN-DRIVEN NAVIGATION
IN 2D MULTISCALE VISUALIZATIONS
WITH SCALABLE INSETS
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Fritz Lekschas @flekschas
Harvard University, School of Engineering
Michael Behrisch, Benjamin Bach, Peter Kerpedjiev,
Nils Gehlenborg, and Hanspeter Pfister
IEEE VIS
Oct 23rd 2019
PATTERN-DRIVEN NAVIGATION
IN 2D MULTISCALE VISUALIZATIONS
WITH SCALABLE INSETS
SCALABLE INSETS
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GOALS
Identify promising regions
for detailed exploration
Find pattern instances
within their context
Compare patterns
across scales
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
See many insets
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
Close to origin
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EFFECTIVENESS
See many insets
Close to origin
Minimize occlusion
Minimize occlusion
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SCALABILITY
Control inset size
Group insets
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GIGAPIXEL IMAGES
Image tiles from The Rio—Hong Kong Connection
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Zoom-invariant size: control
details and context
Dynamic leader line:
avoid clutter
preserve context
INSET DESIGN
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Zoom-invariant size: control
details and context
Dynamic leader line:
avoid clutter
preserve context
INSET DESIGN
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Simulated annealing
Minimize:
inset distance
inset movement
inset overlap
leader-line crossing
Evaluate on zoom
INSET PLACEMENT
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Clustering:
projected distance and area
in pixel space
Stability:
merge and split points differ
avoid abrupt changes
INSET GROUPING
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Image data:
gallery of representatives
highlight diversity
Matrices:
pile of insets
summary statistics
INSET AGGREGATION
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1. User study: Performance comparison in
frequency estimation, pattern search, and pattern comparison
on gigapixel images
Participants: 18
Techniques:
Results: · faster pattern search
· more accurate pattern comparison
· little overhead for frequency estimation
EVALUATION
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6 domain experts: free exploration + think aloud
Easy-to-learn and useful: picked up in ~1min
Inner placement:
guided navigation
Outer placement:
pattern search
& comparisons
2. USER STUDY
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Explore sparsely-distributed local patterns in context
Faster pattern search
Improved navigation
CONCLUSION
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Thank You!
PAPER
vcg.seas.harvard.edu/pubs/scalable-insets
CODE, SLIDES, ETC.
scalable-insets.lekschas.de
CONTACT
@flekschas
FUNDING
CO-AUTHORS
Michael Behrisch, Benjamin Bach, Peter Kerpedjiev
Nils Gehlenborg, and Hanspeter Pfister
is hiring!