MAP-based image tag recommendation using a visual folksonomy

Authors:
Sihyoung Lee;Wesley De Neve;Konstantinos N. Plataniotis;Yong Man Ro
Affiliations:
Image and Video Systems Lab, Korea Advanced Institute of Science and Technology (KAIST), Research Wing R304, 103-6 Munji-dong, Yuseong-gu, Daejeon, South Korea;Image and Video Systems Lab, Korea Advanced Institute of Science and Technology (KAIST), Research Wing R304, 103-6 Munji-dong, Yuseong-gu, Daejeon, South Korea;Multimedia Laboratory, The Edward S. Rogers Sr., Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3GA;Image and Video Systems Lab, Korea Advanced Institute of Science and Technology (KAIST), Research Wing R304, 103-6 Munji-dong, Yuseong-gu, Daejeon, South Korea
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

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.