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Collection Understanding

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. Introduction. Large collection of digital artifacts Actual contents difficult to perceive

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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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