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

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

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

    View Slide

  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

    View Slide

  3. View Slide

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  10. GOALS
    Identify promising regions

    for detailed exploration
    Find pattern instances

    within their context
    Compare patterns

    across scales

    View Slide

  11. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion

    View Slide

  12. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion

    View Slide

  13. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion

    View Slide

  14. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion
    See many insets

    View Slide

  15. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion
    Close to origin

    View Slide

  16. EFFECTIVENESS
    See many insets

    Close to origin
    Minimize occlusion
    Minimize occlusion

    View Slide

  17. SCALABILITY
    Control inset size

    Group insets

    View Slide

  18. View Slide

  19. GIGAPIXEL IMAGES
    Image tiles from The Rio—Hong Kong Connection

    View Slide

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

    avoid clutter

    preserve context
    INSET DESIGN

    View Slide

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

    avoid clutter

    preserve context
    INSET DESIGN

    View Slide

  22. Simulated annealing
    Minimize:

    inset distance

    inset movement

    inset overlap

    leader-line crossing
    Evaluate on zoom
    INSET PLACEMENT

    View Slide

  23. Clustering:

    projected distance and area
    in pixel space
    Stability:

    merge and split points differ

    avoid abrupt changes
    INSET GROUPING

    View Slide

  24. Image data:

    gallery of representatives

    highlight diversity
    Matrices:

    pile of insets

    summary statistics
    INSET AGGREGATION

    View Slide

  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

    View Slide

  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

    View Slide

  27. Explore sparsely-distributed local patterns in context
    Faster pattern search
    Improved navigation
    CONCLUSION

    View Slide

  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!

    View Slide