1. Faceted Metadata in Image Search & Browsing Using Words to Browse a Thousand Images Ka-Ping Yee, Kirsten Swearingen, Kevin Li, Marti Hearst Group for User Interface Research UC Berkeley CHI 2003 Research funded by: NSF CAREER Grant IIS-9984741 IBM Faculty Fellowship

2. M. HearstFaceted Metadata in Search Outline How do people search and browse for images? Current approaches: –Keywords –Spatial similarity Our approach: –Hierarchical Faceted Metadata –Very careful UI design and testing Usability Study Conclusions

3. M. HearstFaceted Metadata in Search How do people want to search and browse images? Ethnographic studies of people who use images intensely: –Finding specific objects is easy –Find images of the Empire State Building –Browsing is difficult –People want to use rich descriptions.

4. M. HearstFaceted Metadata in Search Ethnographic Study Markkula & Sormunen ’00 –Journalists and newspaper editors –Choosing photos from a digital archive Searching for specific objects is trivial Stressed a need for browsing Photos need to deal with themes, places, types of objects, views –Had access to a powerful interface, but it had 40 entry forms and was generally hard to use; no one used it.

5. M. HearstFaceted Metadata in Search Markkula & Sormunen ’00

6. M. HearstFaceted Metadata in Search Query Study Armitage & Enser ’97 –Analyzed 1,749 queries submitted to 7 image and film archives –Classified queries into a 3x4 facet matrix Rio Carnivals: Geo Location x Kind of Event –Concluded that users want to search images according to combinations of topical categories.

7. M. HearstFaceted Metadata in Search Ethnographic Study Ame Elliot ’02 –Architects Common activities: –Use images for inspiration Browsing during early stages of design –Collage making, sketching, pinning up on walls This is different than illustrating powerpoint Maintain sketchbooks & shoeboxes of images –Young professionals have ~500, older ~5k No formal organization scheme –None of 10 architects interviewed about their image collections used indexes Do not like to use computers to find images

8. M. HearstFaceted Metadata in Search Current Approaches to Image Search Keyword based –WebSeek (Smith and Jain ’97) –Commercial web image search systems –Commercial image vendors (Corbis, Getty) –Museum web sites

9. M. HearstFaceted Metadata in Search Current Approaches to Image Search Using Visual “Content” –Extract color, texture, shape QBIC (Flickner et al. ‘95) Blobworld (Carson et al. ‘99) Piction: images + text (Srihari et al. ’91 ’99) –Two uses: Show a clustered similarity space Show those images similar to a selected one –Usability studies: Rodden et al.: a series of studies Clusters don’t work; showing textual labels is promising.

10. M. HearstFaceted Metadata in Search Rodden et al., CHI 2001

11. M. HearstFaceted Metadata in Search Rodden et al., CHI 2001

12. M. HearstFaceted Metadata in Search Rodden et al., CHI 2001

13. M. HearstFaceted Metadata in Search How Best to Support Browsing? To support serendipity, want to view images that are related along multiple dimensions. But clusters are not comprehensible. Instead, allow users to “steer” through the multi-dimensional category space in a flexible manner.

14. M. HearstFaceted Metadata in Search Some Challenges Users don’t like new search interfaces. How to show lots more information without overwhelming or confusing?

15. M. HearstFaceted Metadata in Search Our Approach Integrate the search seamlessly into the information architecture. –Use proper HCI methodologies. Use faceted metadata: –More flexible than canned hyperlinks –Less complex than full search –Help users see where to go next and return to what happened previously

16. M. HearstFaceted Metadata in Search Metadata: data about data Facets: orthogonal categories Time/DateTopicGeoRegion 

17. M. HearstFaceted Metadata in Search Hierarchical Faceted Metadata Example: Biological Subject Headings 1. Anatomy [A] 2. Organisms [B] 3. Diseases [C] 4. Chemicals and Drugs [D] 5. Analytical, Diagnostic and Therapeutic Techniques and Equipment [E] 6. Psychiatry and Psychology [F] 7. Biological Sciences [G] 8. Physical Sciences [H] 9. Anthropology, Education, Sociology and Social Phenomena [I] 10. Technology and Food and Beverages [J] 11. Humanities [K] 12. Information Science [L] 13. Persons [M] 14. Health Care [N] 15. Geographic Locations [Z]

18. M. HearstFaceted Metadata in Search Hierarchical Faced Metadata 1. Anatomy [A] Body Regions [A01] 2. [B] Musculoskeletal System [A02] 3. [C] Digestive System [A03] 4. [D] Respiratory System [A04] 5. [E] Urogenital System [A05] 6. [F] …… 7. [G] 8. Physical Sciences [H] 9. [I] 10. [J] 11. [K] 12. [L] 13. [M]

19. M. HearstFaceted Metadata in Search Hierarchical Faceted Metadata 1. Anatomy [A] Body Regions [A01] Abdomen [A01.047] 2. [B] Musculoskeletal System [A02] Back [A01.176] 3. [C] Digestive System [A03] Breast [A01.236] 4. [D] Respiratory System [A04] Extremities [A01.378] 5. [E] Urogenital System [A05] Head [A01.456] 6. [F] …… Neck [A01.598] 7. [G] …. 8. Physical Sciences [H] 9. [I] 10. [J] 11. [K] 12. [L] 13. [M]

20. M. HearstFaceted Metadata in Search Hierarchical Faceted Metadata 1. Anatomy [A] Body Regions [A01] Abdomen [A01.047] 2. [B] Musculoskeletal System [A02] Back [A01.176] 3. [C] Digestive System [A03] Breast [A01.236] 4. [D] Respiratory System [A04] Extremities [A01.378] 5. [E] Urogenital System [A05] Head [A01.456] 6. [F] …… Neck [A01.598] 7. [G] …. 8. Physical Sciences [H] Electronics 9. [I] Astronomy 10. [J] Nature 11. [K] Time 12. [L] Weights and Measures 13. [M] ….

21. M. HearstFaceted Metadata in Search Hierarchical Faceted Metadata 1. Anatomy [A] Body Regions [A01] Abdomen [A01.047] 2. [B] Musculoskeletal System [A02] Back [A01.176] 3. [C] Digestive System [A03] Breast [A01.236] 4. [D] Respiratory System [A04] Extremities [A01.378] 5. [E] Urogenital System [A05] Head [A01.456] 6. [F] …… Neck [A01.598] 7. [G] …. 8. Physical Sciences [H] Electronics Amplifiers 9. [I] Astronomy Electronics, Medical 10. [J] Nature Transducers 11. [K] Time 12. [L] Weights and Measures 13. [M] ….

22. M. HearstFaceted Metadata in Search Hierarchical Faceted Metadata 1. Anatomy [A] Body Regions [A01] Abdomen [A01.047] 2. [B] Musculoskeletal System [A02] Back [A01.176] 3. [C] Digestive System [A03] Breast [A01.236] 4. [D] Respiratory System [A04] Extremities [A01.378] 5. [E] Urogenital System [A05] Head [A01.456] 6. [F] …… Neck [A01.598] 7. [G] …. 8. Physical Sciences [H] Electronics Amplifiers 9. [I] Astronomy Electronics, Medical 10. [J] Nature Transducers 11. [K] Time 12. [L] Weights and Measures Calibration 13. [M] …. Metric System Reference Standard

23. M. HearstFaceted Metadata in Search Questions we are trying to answer How many facets are allowable? Should facets be mixed and matched? How much is too much? Should hierarchies be progressively revealed, tabbed, some combination? How should free-text search be integrated?

24. M. HearstFaceted Metadata in Search An Important Trend in Information Architecture Design Generating web pages from databases Implications: –Web sites can adapt to user actions –Web sites can be instrumented

25. M. HearstFaceted Metadata in Search A Taxonomy of WebSites low high Complexity of Applications Complexity of Data From: The (Short) Araneus Guide to Website development, by Mecca, et al, Proceedings of WebDB’99, http://www-rocq.inria.fr/~cluet/WEBDB/procwebdb99.html Catalog Sites Web-based Information Systems Web- Presence Sites Service- Oriented Sites

26. M. HearstFaceted Metadata in Search The Interface Design Chess metaphor –Opening –Middle game –End game

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36. M. HearstFaceted Metadata in Search The Interface Design Tightly Integrated Search Supports Expand as well as Refine Dynamically Generated Pages –Paths can be taken in any order Consistent Color Coding Consistent Backup and Bookmarking Standard HTML

37. M. HearstFaceted Metadata in Search What is Tricky About This? It is easy to do it poorly –Yahoo directory structure It is hard to be not overwhelming –Most users prefer simplicity unless complexity really makes a difference It is hard to “make it flow” –Can it feel like “browsing the shelves”?

38. M. HearstFaceted Metadata in Search Project History Identify Target Population –Architects, city planners Needs assessment. –Interviewed architects and conducted contextual inquiries. Lo-fi prototyping. –Showed paper prototype to 3 professional architects. Design / Study Round 1. –Simple interactive version. Users liked metadata idea. Design / Study Round 2: –Developed 4 different detailed versions; evaluated with 11 architects; results somewhat positive but many problems identified. Matrix emerged as a good idea. Metadata revision. –Compressed and simplified the metadata hierarchies

39. M. HearstFaceted Metadata in Search Project History Design / Study Round 3. –New version based on results of Round 2 –Highly positive user response Identified new user population/collection –Students and scholars of art history –Fine arts images Study Round 4 –Compare the metadata system to a strong, representative baseline

40. M. HearstFaceted Metadata in Search New Usability Study Participants & Collection –32 Art History Students –~35,000 images from SF Fine Arts Museum Study Design –Within-subjects Each participant sees both interfaces Balanced in terms of order and tasks –Participants assess each interface after use –Afterwards they compare them directly Data recorded in behavior logs, server logs, paper- surveys; one or two experienced testers at each trial. Used 9 point Likert scales. Session took about 1.5 hours; pay was $15/hour

41. M. HearstFaceted Metadata in Search The Baseline System Floogle Take the best of the existing keyword- based image search systems

42. M. HearstFaceted Metadata in Search Comparison of Common Image Search Systems System Collection# Results /page Catego ries? # Familiar GoogleWeb20No27 AltaVistaWeb15No8 CorbisPhotos9-36No8 GettyPhotos, Art 12-90Yes6 MS OfficePhotos, Clip art 6-100YesN/A ThinkerFine arts images 10Yes4 BASELINEFine arts images 40YesN/A

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47. M. HearstFaceted Metadata in Search Evaluation Quandary How to assess the success of browsing? –Timing is usually not a good indicator –People often spend longer when browsing is going well. Not the case for directed search –Can look for comprehensiveness and correctness (precision and recall) … –… But subjective measures seem to be most important here.

48. M. HearstFaceted Metadata in Search Hypotheses We attempted to design tasks to test the following hypotheses: –Participants will experience greater search satisfaction, feel greater confidence in the results, produce higher recall, and encounter fewer dead ends using FC over Baseline –FC will perceived to be more useful and flexible than Baseline –Participants will feel more familiar with the contents of the collection after using FC –Participants will use FC to create multi-faceted queries

49. M. HearstFaceted Metadata in Search Four Types of Tasks –Unstructured (3): Search for images of interest –Structured Task (11-14): Gather materials for an art history essay on a given topic, e.g. Find all woodcuts created in the US Choose the decade with the most Select one of the artists in this periods and show all of their woodcuts Choose a subject depicted in these works and find another artist who treated the same subject in a different way. –Structured Task (10): compare related images Find images by artists from 2 different countries that depict conflict between groups. –Unstructured (5): search for images of interest

50. M. HearstFaceted Metadata in Search Other Points Participants were NOT walked through the interfaces. The wording of Task 2 reflected the metadata; not the case for Task 3 Within tasks, queries were not different in difficulty (t’s 0.05 according to post-task questions) Flamenco is and order of magnitude slower than Floogle on average. –In task 2 users were allowed 3 more minutes in FC than in Baseline. –Time spent in tasks 2 and 3 were significantly longer in FC (about 2 min more).

51. M. HearstFaceted Metadata in Search Results Participants felt significantly more confident they had found all relevant images using FC (Task 2: t(62)=2.18, p<.05; Task 3: t(62)=2.03, p<.05) Participants felt significantly more satisfied with the results (Task 2: t(62)=3.78, p<.001; Task 3: t(62)=2.03, p<.05) Recall scores: –Task2a: In Baseline 57% of participants found all relevant results, in FC 81% found all. –Task 2b: In Baseline 21% found all relevant, in FC 77% found all.

52. M. HearstFaceted Metadata in Search Post-Interface Assessments All significant at p<.05 except simple and overwhelming

53. M. HearstFaceted Metadata in Search Perceived Uses of Interfaces Baseline FC

54. M. HearstFaceted Metadata in Search Post-Test Comparison 1516 230 129 428 823 624 283 131 229 FC Baseline Find images of roses Find all works from a given period Find pictures by 2 artists in same media Which Interface Preferable For:

55. M. HearstFaceted Metadata in Search Post-Test Comparison 1516 230 129 428 823 624 283 131 229 FC Baseline Overall Assessment: More useful for your tasks Easiest to use Most flexible More likely to result in dead ends Helped you learn more Overall preference Find images of roses Find all works from a given period Find pictures by 2 artists in same media Which Interface Preferable For:

56. M. HearstFaceted Metadata in Search Facet Usage Facets driven largely by task content –Multiple facets 45% of time in structured tasks For unstructured tasks, –Artists (17%) –Date (15%) –Location (15%) –Others ranged from 5-12% –Multiple facets 19% of time From end game, expansion from –Artists (39%) –Media (29%) –Shapes (19%)

57. M. HearstFaceted Metadata in Search Qualitative Observations Baseline: –Simplicity, similarity to Google a plus –Also noted the usefulness of the category links FC: –Starting page “well-organized”, gave “ideas for what to search for” –Query previews were commented on explicitly by 9 participants –Commented on matrix prompting where to go next 3 were confused about what the matrix shows –Generally liked the grouping and organizing –End game links seemed useful; 9 explicitly remarked positively on the guidance provided there. –Often get requests to use the system in future

58. M. HearstFaceted Metadata in Search Study Results Summary Strongly positive results for the faceted metadata interface. Moderate use of multiple facets. Strong preference over the current state of the art. –Chair of Architecture Dept: “It felt like I was browsing the shelves!” –This kind of enthusiasm is not seen in similarity- based image search interfaces. Hypotheses are supported.

59. M. HearstFaceted Metadata in Search Implementation All open source code –Mysql database –Python web server (Webkit) –Python code –Lucene search engine (java)

60. M. HearstFaceted Metadata in Search Metadata Availability Many collections already have rich metadata associated with them. Automated methods are improving. This tool may be helpful for resolving metadata creation wars.

61. M. HearstFaceted Metadata in Search Summary Usability studies done on 3 collections: –Recipes: 13,000 items –Architecture Images: 40,000 items –Fine Arts Images: 35,000 items Conclusions: –Users like and are successful with the dynamic faceted hierarchical metadata, especially for browsing tasks –Very positive results, in contrast with studies on earlier iterations –Note: it seems you have to care about the contents of the collection to like the interface

62. M. HearstFaceted Metadata in Search Advantages of the Approach Supports different search types –Highly constrained known-item searches –Open-ended, browsing tasks –Can easily switch from one mode to the other midstream –Can both expand and refine Allows different people to add content without breaking things Can make use of standard technology

63. M. HearstFaceted Metadata in Search Other Domains Applying this to –Text Tobacco Documents Archives Medline biomedical texts –Products/Catalogs Don’t have a collection; would like one

64. M. HearstFaceted Metadata in Search Future Work What about information visualization? How to integrate with relevance feedback (more like this)? How to incorporate user preferences and past behavior? How to combine facets to reflect tasks?

65. 65 Thanks to: Andrea Sahli Rashmi Sinha NSF CAREER Grant IIS-9984741 IBM Faculty Fellowship Try the Demo: flamenco.berkeley.edu