The document discusses tagging images for findability in a digital asset management system. It covers assessing images, tagging options like free text descriptions or controlled vocabularies, and challenges. Controlled vocabularies foster consistency but are complex to design, while folksonomies can cut costs but lack precision. Images should be tagged with basic attributes, depicted contents, and abstract concepts. Challenges include specificity, focus, and applying abstract concepts. A custom approach is needed considering images and users.

1. Tagging Images for Findability: making your DAM
system work for you
1 July 2009
Digital Asset Management
London
Ian Davis
Global Taxonomy Delivery Manager
Dow Jones Client Solutions
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2. What will we cover?
Tagging images for findability:
 Image assessment
 Image tagging options
 Image tagging actions
 Image tagging challenges
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3. Image Categorisation
Image Findability Assessment:
3) The image content
4) The current metadata
5) Users and potential users
6) Create metadata to link users to images
7) Create/maintain feedback loops
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4. Image Categorisation
Textual approaches:
3) Free text descriptions
4) Controlled vocabulary applications
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5. Image Categorisation
Free text descriptions:
 Writing about individual images or groups of images
without using any controlled language
 Titles – 3 to 4 words
 Captions – 3 to 4 sentences
 Free text keywords or tags
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6. Image Categorisation
Free text?
 Easy to set up, needs little preparation
 Provides useful context
But:
 Time consuming and labour intensive
 Lacks precision and consistency
 Hard to support searching and browsing
 Hard wired access points
 Context can be lacking
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7. Image Categorisation
Controlled vocabulary?
 Well known, popular and proven to work well enough
 Fosters precision and consistency
 Strong search and browse experience
But:
 Can be complex to design and deploy
 Labour intensive, time consuming
 Hard wired access points
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8. Image Categorisation
Can folksonomy help?
 Connects with content creators and users –
passionate community involvement
 Enriches classification and retrieval language
 Creates candidate or approved access points
 Cuts down on classification costs
 Helps to keep language fresh
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9. Image Categorisation
Finding:
 Basic attributes
 Depicted content
 Abstract concepts
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10. Image Categorisation
Finding:
 Basic attributes:
 Formats
 Photographic techniques
 Creator names
 Creation dates
 Location and GPS co-ordinates
 Campaign names
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11. Image Categorisation
Finding:
 Depicted content:
 Actions and events – smiling, conferences
 Animals and plants
 People’s role and occupations, ethnicity, nationality
and anatomy
 Topography – mountains, valleys, oceans
 Structures – skyscrapers, houses, office buildings
 Varied objects
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12. Image Categorisation
Finding:
 Abstract concepts (aboutness):
 Emotions – love, hate
 Characteristics – fast, slow
 Moods – dreamy, eerie, disturbing
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13. Tagged Example
“Posing Businesswomen”
“Four stock photography models
pose for a business shoot.”
“Four people, Women, Adults
Females, Businesswomen
Suits, Looking at camera, Posing
Outdoors, Sitting, Standing
Caucasians, 20s, Sunlight
Determined, Strong”
Eman Winston
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14. Watch out for - Specificity
People want different levels of specificity
Puyodead
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15. Watch out for - Focus
Fool-On-The-Hill
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16. Watch out for - Focus
Thorne Enterprises
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17. Watch out for - Focus
deltaMike
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18. Watch out for - Abstracts concepts
Abstract concepts are often popular with users
but tricky to apply to images
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19. Watch out for - Abstracts concepts
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20. Watch out for - Abstracts concepts
Jim Gize
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21. Watch out for - Abstracts concepts
Darran Hester
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22. Watch out for - Abstracts concepts
James Bowe
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23. Watch out for - Abstracts concepts
SoulRider
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24. Watch out for – simple images
Some of the simplest images can be less than
simple...
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25. Watch out for – simple images
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26. Watch out for – simple images
Sister72
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27. Conclusion
 Don’t rush into things – plan fully first
 Don’t treat images like text
 Consider the images and the users
 Construct a customised approach to meet
your needs
 No magic technology
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28. Thank you!
ian.davis@dowjones.com
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