1. Collection Understanding Michelle Chang, John J. Leggett, Richard Furuta, Andruid Kerne Texas A&M University 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 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 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 Find specific artifacts Prior knowledge of metadata Define queries

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

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

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

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

9. Ambient Displays Dangling String Tangible Bits Informative Art 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 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 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 Limited screen space Entire result set not visible Requires large amount of pointing and clicking

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

14. Streaming Collage Collage is “an assembly of diverse fragments” Streaming – constructed dynamically in time 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 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 Continuous Process of: Interactively filtering metadata Generating dynamic collage Temporal and Spatial

17. Demonstration: Metadata Filtering

18. Demonstration: Streaming Collage

19. Demonstration: Subcollections

22. Ambient Slideshow Peripheral Display Chance encounters Slowly reveals artifacts in the collection 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 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 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 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 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 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 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. 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 http://www.csdl.tamu.edu/~mchang/thesis.html mchang@csdl.tamu.edu