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Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets

Ab6e0a2ba101a2eb0a975b1ef915e85a?s=47 Fritz Lekschas
October 23, 2019

Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets

Scalable Insets is a novel technique for pattern-guided navigation in multiscale visualizations such as geographics maps, gigapixel images, or large matrices like genome interaction maps.

These slides are from my paper talk at IEEE VIS InfoVis 2019.

Paper: https://vcg.seas.harvard.edu/pubs/scalable-insets
Video: https://youtu.be/7Bn4mNLl3WQ
Code etc: https://scalable-insets.lekschas.de

Ab6e0a2ba101a2eb0a975b1ef915e85a?s=128

Fritz Lekschas

October 23, 2019
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  1. 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
  2. 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
  3. None
  4. None
  5. None
  6. None
  7. None
  8. None
  9. None
  10. GOALS Identify promising regions
 for detailed exploration Find pattern instances


    within their context Compare patterns
 across scales
  11. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion

  12. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion

  13. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion

  14. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion See

    many insets
  15. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion Close

    to origin
  16. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion Minimize

    occlusion
  17. SCALABILITY Control inset size
 Group insets

  18. None
  19. GIGAPIXEL IMAGES Image tiles from The Rio—Hong Kong Connection

  20. Zoom-invariant size: control details and context Dynamic leader line:
 avoid

    clutter
 preserve context INSET DESIGN
  21. Zoom-invariant size: control details and context Dynamic leader line:
 avoid

    clutter
 preserve context INSET DESIGN
  22. Simulated annealing Minimize:
 inset distance
 inset movement
 inset overlap
 leader-line

    crossing Evaluate on zoom INSET PLACEMENT
  23. Clustering:
 projected distance and area in pixel space Stability:
 merge

    and split points differ
 avoid abrupt changes INSET GROUPING
  24. Image data:
 gallery of representatives
 highlight diversity Matrices:
 pile of

    insets
 summary statistics INSET AGGREGATION
  25. 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
  26. 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
  27. Explore sparsely-distributed local patterns in context Faster pattern search Improved

    navigation CONCLUSION
  28. 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!