Learning to Recommend Tags for On-line Photos
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. Experiwith ments demonstrate the improved performance of the proposed approach compared the state-of-the-art techniques in the literature.
KeywordsLatent Semantic Analysis Collaborative Filter Visual Content Computer Support Cooperative Work Fuzzy Association Rule
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