Facets of user-assigned tags and their
effectiveness in image retrieval
Nicky Ransom
University for the Creative Arts
Tags
Election night crowd, Wellington, 1931
Photographer: William Hall Raine
Election night crowd, Wellington, 1931
Reference number: 1/2-066547-F
Original negative
Photographic Archive, Alexander Turnbull Library
•William Hall Raine
•crowd
•men
•hats
•street
•night
•lighting
•faces
•sea of people
•people
•watching
•event
•election
•results
•populated
Background to research topic
• Growth in number of images online
• Accurate and comprehensive indexing is
critical to make online content accessible
• But visual materials are difficult to index
Approaches to image indexing
• Concept-based indexing – assigning index
terms to describe the subject of an image
Approaches to image indexing
• Concept-based indexing – assigning index
terms to describe the subject of an image
• Search engine indexing – index terms
automatically created from data related to an
image
Approaches to image indexing
• Concept-based indexing – assigning index
terms to describe the subject of an image
• Search engine indexing – index terms
automatically created from data related to an
image
• Content-based indexing – using automatic
processing to index image attributes such as
colour, texture and shape
CIRES: Content Based Image REtrieval System
User tagging
Research question
To find out value of tags for image retrieval by
investigating whether the terms used to
describe images in tags are similar to the
terms used to search for images.
– Which image facets are described in user tags?
– How do these compare to those found in image
queries?
– What are the implications for future use of tagging
for online indexing?
Shatford’s matrix
Who?
What?
Where?
When?
Specific
Generic
Abstract
Individually named
person, group or
thing (S1)
eg Napoleon
Kind of person,
group or thing (G1)
Mythical or fictitious
being (A1)
eg Skyscraper
eg King Arthur
Individually named
event or action (S2)
Kind of event, action
or condition (G2)
Emotion or
abstraction (A2)
eg London Olympics
eg Football game
eg Anger
Individually named
geographical location
(S3)
eg New York
Kind of place:
geographical or
architectural (G3)
eg Forest
Place symbolised
(A3)
Linear time: date or
period (S4)
Cyclical time: season
or time of day (G4)
eg 2010
eg Spring
Emotion/abstraction
symbolised by time
(A4)
eg Father Time
eg Paradise
Armitage, L., & Enser, P. (1997). Analysis of user need in image archives Journal of Information Science, 23(4), 287-299.
Research methodology
• Small scale study using 250 images and
associated tags on Flickr
• Tags categorised using facets from ‘Shatford’s
matrix’
• Comparisons made with results of previous
research into user queries
Comparison of tags and queries (1)
Comparison of tags and queries (2)
Comparison of tags and queries (3)
Factors affecting results
• Limited sample size – only 250 images
• Use of Flickr as domain for study – only 38% of
users apply tags
• Subjectivity of categorising tags – only one
person assigning tags to categories
• Suitability of Shatford’s matrix – 22% of terms
could not be categorised
• Lack of online query studies with which to
compare the results
Conclusion
• Broad similarities between the image facets
used in queries and image tags
• But differences in the level of specificity
• Need to develop systems to bridge this gap
• Consider the value of tags for browsing
systems
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Facets of user-assigned tags and their effectiveness in image