Mining multi-tag association for image tagging
(Citations: 3)
Automatic media tagging plays a critical role in modern tag-based media retrieval systems. Existing tagging schemes mostly
perform tag assignment based on community contributed media resources, where the tags are provided by users interactively.
However, such social resources usually contain dirty and incomplete tags, which severely limit the performance of these tagging
methods. In this paper, we propose a novel automatic image tagging method aiming to automatically discover more complete tags
associated with information importance for test images. Given an image dataset, all the near-duplicate clusters are discovered.
For each near-duplicate cluster, all the tags occurring in the cluster form the cluster’s “document”. Given a test image,
we firstly initialize the candidate tag set from its near-duplicate cluster’s document. The candidate tag set is then expanded
by considering the implicit multi-tag associations mined from all the clusters’ documents, where each cluster’s document is
regarded as a transaction. To further reduce noisy tags, a visual relevance score is also computed for each candidate tag
to the test image based on a new tag model. Tags with very low scores can be removed from the final tag set. Extensive experiments
conducted on a real-world web image dataset—NUS-WIDE, demonstrate the promising effectiveness of our approach.