MAP-based image tag recommendation using a visual folksonomy

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Descriptive tags are needed to enable efficient and effective search in vast collections of images. Tag recommendation represents a trade-off between automatic image annotation techniques and manual tagging. In this letter, we formulate image tag recommendation as a maximum a posteriori (MAP) problem, making use of a visual folksonomy. A folksonomy can be seen as a collaboratively created set of metadata for informal social classification. Our experimental results show that the use of a visual folksonomy for image tag recommendation has two significant benefits, compared to a conventional approach using a limited concept vocabulary. First, our tag recommendation technique can make use of an unrestricted and rich concept vocabulary. Second, our approach is able to recommend a higher number of correct tags. (c) 2009 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2010-07
Language
English
Article Type
Article
Keywords

ANNOTATION

Citation

PATTERN RECOGNITION LETTERS, v.31, no.9, pp.976 - 982

ISSN
0167-8655
DOI
10.1016/j.patrec.2009.12.024
URI
http://hdl.handle.net/10203/100880
Appears in Collection
EE-Journal Papers(저널논문)
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