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-9984741 1

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 2

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.

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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.

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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.

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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.

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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 7

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.

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Rodden et al., CHI 2001

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Rodden et al., CHI 2001

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Rodden et al., CHI 2001

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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 12

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!

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Some Challenges

• • Users don’t like new search interfaces.

How to show lots more information without overwhelming or confusing?

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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 15

Faceted Metadata

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Metadata: data about data Facets: orthogonal categories GeoRegion + Time/Date + Topic 17

Faceted Metadata: Biomedical MeSH (Medical Subject Headings) www.nlm.nih.org/mesh 18

Mesh Facets (one level expanded) 19

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?

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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 21

A Taxonomy of WebSites

high

Complexity of Data

low

Catalog Sites Web-based Information Systems Web Presence Sites Service Oriented Sites

low high

Complexity of Applications 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

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The Flamenco Interface

• • • • • Nine hierarchical facets – Matrix – SingleTree Chess metaphor – Opening – – Middle game End game Tightly Integrated Search Expand as well as Refine Intermediate pages for large categories 23

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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”?

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How

NOT

to do it

• • Yahoo uses faceted metadata poorly in both their search results and in their top-level directory They combine region + other hierarchical facets in awkward ways 34

Yahoo’s use of facets

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Yahoo’s use of facets

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Yahoo’s use of facets

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Yahoo’s use of facets

 Where is Berkeley?

 College and University > Colleges and Universities >United States > U > University of California > Campuses > Berkeley  U.S. States > California > Cities >Berkeley > Education > College and University > Public > UC Berkeley 38

Problem with Metadata Previews as Currently Used – – – – Hand edited, predefined Not tailored to task as it develops Not personalized Often not systematically integrated with search, or within the information architecture in general 39

HCI Methodology

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Identify Target Population

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Needs assessment.

What to people want; how to they work? – –

Lo-fi prototyping

. Produce cheap (throw-away) prototypes Get feedback from target population –

Design / Study Round 1

. Simple interactive version. See if main ideas work.

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Design / Study Round 2:

More thorough interactive version; more graphics. Begin to fine-tune, fix remaining major problems –

Design / Study Round 3:

Continue to fine-tune. Introduce more advanced features.

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Our Project History

• • • • • •

Identify Target Population

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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 41

Our 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 42

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 43

The Baseline System

• • Floogle Take the best of the existing keyword based image search systems 44

Comparison of Common Image Search Systems System Collection # Results /page Catego ries?

# Familiar Google AltaVista Web Web Corbis Photos Getty Photos, Art MS Office Photos, Clip art Thinker Fine arts images BASELINE Fine arts images 20 15 9-36 12-90 6-100 10 40 No No No Yes Yes Yes Yes 27 8 8 6 N/A 4 N/A 45

sword 46

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Evaluation Quandary

• How to assess the success of browsing?

– – – – Timing is usually not a good indicator People often spend is going well.

longer when browsing • Not the case for directed search Can look for comprehensiveness and correctness (precision and recall) … … But subjective measures seem to be most important here.

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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 51

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 52

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<1.7, p >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).

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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.

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Post-Interface Assessments

All significant at p<.05 except simple and overwhelming 55

Perceived Uses of Interfaces

What is interface useful for?

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6.44

7.97

5.47

7.91

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6.64

4.91

6.16

Useful for my coursework Useful for exploring an unfamiliar collection Useful for finding a particular image Useful for seeing relationships b/w images Baseline SHASTA DENALI FC 56

Post-Test Comparison

Which Interface Preferable For: Find images of roses Find all works from a given period Find pictures by 2 artists in same media 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 Baseline

15 2 1 4 8 6 28 1 2

FC

16 30 29 28 23 24 3 31 29 57

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%) 58

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 59

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.

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Other Domains

• Applying this to – – Text • Tobacco Documents Archives • Medline biomedical texts Products/Catalogs • Don’t have a collection; would like one 61

Implementation

• All open source code – – – – Mysql database Python web server (Webkit) Python code Lucene search engine (java) 62

Summary and Conclusions

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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 64

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 65

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 too One is very similar now: endeca.com

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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 67

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?

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The Flamenco Project Team

Kevin Chen Ame Elliott Jennifer English Kevin Li Rashmi Sinha Kirsten Swearingen Ping Yee http://flamenco.berkeley.edu

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Thank you!

For more information: flamenco.berkeley.edu

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