TY - GEN
T1 - Learning to recommend tags for on-line photos
AU - Wang, Zheshen
AU - Li, Baoxin
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Recommending text tags for on-line photos is useful for on-line photo services. We propose a novel approach to tag recommendation by utilizing both the underlying semantic correlation between visual contents and text tags and the tag popularity learnt from realistic on-line photos. We apply our approach to a database of real on-line photos and evaluate its performance by both objective and subjective evaluation. Experiments demonstrate the improved performance of the proposed approach compared with the state-of-the-art techniques in the literature.
AB - Recommending text tags for on-line photos is useful for on-line photo services. We propose a novel approach to tag recommendation by utilizing both the underlying semantic correlation between visual contents and text tags and the tag popularity learnt from realistic on-line photos. We apply our approach to a database of real on-line photos and evaluate its performance by both objective and subjective evaluation. Experiments demonstrate the improved performance of the proposed approach compared with the state-of-the-art techniques in the literature.
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U2 - 10.1007/978-1-4419-0056-2_29
DO - 10.1007/978-1-4419-0056-2_29
M3 - Conference contribution
AN - SCOPUS:84900217141
SN - 9781441900555
T3 - Social Computing and Behavioral Modeling
SP - 227
EP - 235
BT - Social Computing and Behavioral Modeling
PB - Springer Science and Business Media, LLC
T2 - 2nd International Workshop on Social Computing, Behavioral Modeling and Prediction, SBP 2009
Y2 - 31 March 2009 through 1 April 2009
ER -