Learning to recommend tags for on-line photos

Abstract

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. © Springer Science + Business Media, LLC 2009.