Contenu connexe Similaire à "Tagging Images For Findability: Making Your DAM Work For You", Ian Davis (15) "Tagging Images For Findability: Making Your DAM Work For You", Ian Davis1. 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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