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World Wide Web

, Volume 14, Issue 2, pp 133–156 | Cite as

Mining multi-tag association for image tagging

  • Yang Yang
  • Zi Huang
  • Heng Tao ShenEmail author
  • Xiaofang Zhou
Article

Abstract

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.

Keywords

image tagging tag completion tag denoising weighted association rule mining 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Yang Yang
    • 1
  • Zi Huang
    • 1
  • Heng Tao Shen
    • 1
    Email author
  • Xiaofang Zhou
    • 1
  1. 1.School of Information Technology & Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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