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Collection Understanding

This work explores advanced methods for understanding large collections of digital artifacts by focusing on their essence rather than just retrieval. Traditional image retrieval methods fall short in accessing actual content within a collection; this study introduces innovative techniques such as streaming collages and metadata filtering to create dynamic visualizations. By engaging users in constructing their own relationships with artifacts, we aim to improve the overall experience and usability of digital collections. Insights from user studies are used to inform best practices for intuitive interfaces.

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Collection Understanding

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  1. Collection Understanding Michelle Chang, John J. Leggett, Richard Furuta, Andruid Kerne Texas A&M University J. Patrick Williams, Samuel A. Burns, Randolph G. Bias University of Texas at Austin

  2. Introduction • Large collection of digital artifacts • Actual contents difficult to perceive • Image retrieval methods are insufficient

  3. Collection Understanding • Understand the essence of the collection by focusing on the artifacts • Comprehensive view • Not locating specific artifacts

  4. Collection Understanding (CU) vs. Information Retrieval (IR) • Find specific artifacts • Prior knowledge of metadata • Define queries

  5. Related Work • Collages • Photo Browsers • Image Browsers • Ambient Displays

  6. Collage • combinFormation • Collaborage • Notification Collage • Aesthetic Information Collages • Video Collage

  7. Photo Browsers • Calendar Browser • Hierarchical Browser • FotoFile • PhotoFinder • PhotoMesa

  8. Image Browsers • Zoomable Image Browser • Strip-Browser • Flamenco Image Browser

  9. Ambient Displays • Dangling String • Tangible Bits • Informative Art

  10. Problems with Querying by Metadata • Currently the most used method • Two levels: collection, artifact • Creator/maintainer/collector defines metadata • Time-consuming • Vague

  11. Problems with Browsing • Pre-defined and fixed structure • Requires large amount of navigation (pointing and clicking) • Narrows a collection

  12. Problems with Scrolling • Limited screen space • Entire result set not visible • Requires large amount of pointing and clicking

  13. Visualization • Streaming Collage • Ambient Slideshow • Variably Gridded Thumbnails

  14. Streaming Collage • Collage is “an assembly of diverse fragments” • Streaming – constructed dynamically in time

  15. Metadata Filtering • Modifying metadata fields and values • Expand result set • Constrain result set

  16. Connecting Streaming Collage with Metadata Filtering • Continuous Process of: Interactively filtering metadata Generating dynamic collage • Temporal and Spatial

  17. Demonstration: Metadata Filtering

  18. Demonstration: Streaming Collage

  19. Demonstration: Subcollections

  20. Demonstration: Subcollections

  21. Demonstration: Subcollections

  22. Ambient Slideshow • Peripheral Display • Chance encounters • Slowly reveals artifacts in the collection

  23. Demonstration: Ambient Picasso

  24. Demonstration: Variably Gridded Thumbnails

  25. Variably Gridded Thumbnails • Relevance measure • Full-text search • Grid of thumbnails • Grid element’s background color varies

  26. Evaluation • Independent evaluation • Usability study gauged intuitiveness of interface • 15 graduate students: UT at Austin

  27. No Directed Tasks • Users “queried the database” • Didn’t right-click on any images • Didn’t use metadata filtering

  28. Directed Tasks • Successfully created collages • Right-clicked on images • Used metadata filtering

  29. Conclusions from study • Improvements for intuitive interface • Initial engagement • Metadata Filtering form & controls • Help menu • Hint for no results

  30. Summary • Collection understanding shifts the traditional focus of image retrieval • Inspire users to derive their own relationships by focusing on artifacts • Collection insight increases

  31. Acknowledgments • Dr. Enrique Mallen, The On-Line Picasso Project • The Humanities Informatics Initiative, Telecommunications and Informatics Task Force, Texas A&M University.

  32. http://www.csdl.tamu.edu/~mchang/thesis.html mchang@csdl.tamu.edu

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