Tag Relevance Fusion for Social Image Retrieval
This addresses image retrieval challenges for users of social media platforms, but it is incremental as it extends existing tag relevance estimation methods.
The paper tackles the problem of retrieving social-networked images by measuring tag relevance, introducing tag relevance fusion to improve retrieval performance, with experiments showing it leads to better results and unsupervised fusion being as effective as supervised without training.
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.