1 Using Words to Search a Thousand Images Hierarchical Faceted Metadata in Search & Browsing Marti Hearst SIMS, UC Berkeley Research funded by: NSF CAREER Grant IIS

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

3 How do people want to search and browse images? Ethnographic studies of people who use images intensely find: –Find specific objects is easy Find images of the Empire State Building –Browsing is hard, and people want to use rich descriptors.

4 Ethnographic Studies Garber & Grunes ’92 –Art directors, art buyers, stock photo researchers –Search for appropriate images is iterative –After specifying and weighting criteria, searchers view retrieved images, then Add restrictions Change criteria Redefine Search –Concept starts out loosely defined, then becomes more refined.

5 Ethnographic Studies Markkula & Sormunen ’00 –Journalists and newspaper editors –Choosing photos from a digital archive Stressed a need for browsing Searching for specific objects is trivial 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.

6 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 –Conclude that users want to search images according to combinations of topical categories.

7 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 Current Approaches to Image Search Using Visual “Content” –Extract color, texture, shape QBIC (Flickner et al. ‘95) Blobworld (Carson et al. ‘99) Body Plans (Forsyth & Fleck ‘00) 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.

9 Rodden et al., CHI 2001

10 Rodden et al., CHI 2001

11 Rodden et al., CHI 2001

12 Current Approaches to Image Search Keyword based –WebSeek (Smith and Jain ’97) –Commercial image vendors (Corbis, Getty) –Commercial web image search systems –Museum web sites

13 A Disconnect Why are image search systems built so differently from what people want? –An image is worth a thousand words. –But the converse has merit too!

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

15 Our Approach Integrate the search seamlessly into the information architecture. Use proper HCI methodologies. Use faceted metadata

16 Faceted Metadata

17 What are facets? Sets of categories, each of which describe a different aspect of the objects in the collection. Each of these can be hierarchical. (Not necessarily mutually exclusive nor exhaustive, but often that is a goal.) Time/DateTopicRoleGeoRegion 

18 Facet example: Recipes Course Main Course Cooking Method Stir-fry Cuisine Thai Ingredient Red Bell Pepper Curry Chicken

19 Goal: assign labels from facets

20 Motivation Description: 19th c. paint horse; saddle and hackamore; spurs; bandana on rider; old time cowboy hat; underchin thong; flying off. Nature Animal Mammal Horse Occupations Cowboy Clothing Hats Cowboy Hat Media Engraving Wood Eng. Location North America America

21 Hierarchical Faceted Metadata A simplification of knowledge representation Does not represent relationships directly BUT can be understood well by many people when browsing rich collections of information.

22 How to Put In an Interface? Some Challenges: Users don’t like new search interfaces. How to show lots of information without overwhelming or confusing?

23 A Solution (The Flamenco Project) Use proper HCI methods. Organize search results according to the faceted metadata so navigation looks similar throughout –Easy to see what to go next, were you’ve been –Avoids empty result sets –Integrates seamlessly with keyword search

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33 What is Tricky About This? It is easy to do it poorly –See Yahoo example 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”?

34 HCI Method (Human-Computer Interaction) 1.Identify Target Population 2.Needs assessment. –What to people want; how to they work? 3.Lo-fi prototyping. –Produce cheap (throw-away) prototypes –Get feedback from target population 4.Design / Study Round 1. –Simple interactive version. See if main ideas work. 5.Design / Study Round 2: –More thorough interactive version; more graphics. Begin to fine-tune, fix remaining major problems 6.Design / Study Round 3: –Continue to fine-tune. Introduce more advanced features.

35 Using HCI Method (Human-Computer Interaction) 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.

36 Method (cont) Metadata revision. –Compressed and simplified the metadata hierarchies 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

37 Final 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

38 The Baseline System Floogle Take the best of the existing keyword- based image search systems

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43 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.

44 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

45 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

46 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).

47 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.

48 Post-Interface Assessments All significant at p<.05 except simple and overwhelming

49 Perceived Uses of Interfaces Baseline FC

50 Post-Test Comparison FCBaseline 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:

51 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%)

52 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

53 Study Results Summary Overwhelmingly positive results for the faceted metadata interface. Somewhat heavy use of multiple facets. Strong preference over the current state of the art. This result not seen in similarity-based image search interfaces. Hypotheses are supported.

54 Implementation All open source code –Mysql database –Python web server (Webkit) –Python code –Lucene search engine (java)

55 Summary and Conclusions

56 Summary We have addressed several interface problems: –How to seamlessly integrate metadata previews with search Show search results in metadata context “Disambiguate” search terms –How to show hierarchical metadata from several facets The “matrix” view Show one level of depth in the “matrix” view –How to handle large metadata categories Use intermediate pages –How to support expanding as well as refining

57 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

58 Summary Validating an approach to web site search –Use hierarchical faceted metadata dynamically, integrated with search Many difficult design decisions –Iterating and testing was key Bits and pieces were there in industry –The approach is being picked up all over –There are providers (endeca, siderean)

59 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

60 Some Unanswered Questions How to integrate with relevance feedback (more like this)? –Would like to use blobworld-like features How to incorporate user preferences and past behavior? How to combine facets to reflect tasks?

61 The Flamenco Project Team Kevin Chen Ame Elliott Jennifer English Kevin Li Rashmi Sinha Kirsten Swearingen Ping Yee

62 Thank you! flamenco.berkeley.edu For more information: