Abstract

Abstract Due to the subjective nature of social tag-ging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increas-ing amounts of social-networked images. Witnessing the limit of a single measurement of tag relevance, we intro-duce in this paper tag relevance fusion as an extension to methods for tag relevance estimation. We present a systematic study, covering tag relevance fusion in early and late stages, and in supervised and unsupervised set-tings. Experiments on a large present-day benchmark set show that tag relevance fusion leads to better image retrieval. Moreover, unsupervised tag relevance fusion is found to be practically as effective as supervised tag relevance fusion, but without the need of any training efforts. This finding suggests the potential of tag rele-vance fusion for real-world deployment.