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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What will we cover?
Tagging images for findability:
 Image assessment
 Image tagging options
 Image tagging actions
 Image tagging challenges
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Image Categorisation
Image Findability Assessment:
• The image content
• The current metadata
• Users and potential users
• Create metadata to link users to images
• Create/maintain feedback loops
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Image Categorisation
Textual approaches:
• Free text descriptions
• Controlled vocabulary applications
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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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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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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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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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Image Categorisation
Finding:
 Basic attributes
 Depicted content
 Abstract concepts
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Image Categorisation
Finding:
 Basic attributes:
 Formats
 Photographic techniques
 Creator names
 Creation dates
 Location and GPS co-ordinates
 Campaign names
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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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Image Categorisation
Finding:
 Abstract concepts (aboutness):
 Emotions – love, hate
 Characteristics – fast, slow
 Moods – dreamy, eerie, disturbing
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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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Watch out for - Specificity
People want different levels of specificity
Puyodead
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Watch out for - Focus
Fool-On-The-Hill
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Watch out for - Focus
Thorne Enterprises
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Watch out for - Focus
deltaMike
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Watch out for - Abstracts concepts
Abstract concepts are often popular with users
but tricky to apply to images
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Watch out for - Abstracts concepts
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Watch out for - Abstracts concepts
Jim Gize
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Watch out for - Abstracts concepts
Darran Hester
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Watch out for - Abstracts concepts
James Bowe
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Watch out for - Abstracts concepts
SoulRider
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Watch out for – simple images
Some of the simplest images can be less than
simple...
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Watch out for – simple images
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Watch out for – simple images
Sister72
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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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Thank you!
ian.davis@dowjones.com
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