  Due to the subjective nature of social tagging, measuring the relevance of
social tags with respect to the visual content is crucial for retrieving the
increasing amounts of social-networked images. Witnessing the limit of a single
measurement of tag relevance, we introduce 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 settings. 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 relevance fusion
for real-world deployment.
