Dong Liu
Xian-Sheng Hua
ABSTRACT
Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection
show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into three applications: (1) tag-based image search, (2) tag recommendation, and (3) group recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.
- Flickr. http://www.flickr.com.Google Scholar
- M. Ames and M. Naaman. Why We Tag: Motivations for Annotation in Mobile and Online Media. In Proceeding of the SIGCHI Conference on Human Factors in Computing System, 2007. Google ScholarDigital Library
- L. S. Kennedy, S. F. Chang, and I. V. Kozintsev. To Search or To Label? Precdicting the Performance of Search--Based Automatic Image Classifiers. In Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, 2006. Google ScholarDigital Library
- M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based Multimedia Information Retrieval: State of the Art and Challenges. In ACM Transactions on Multimedia Computing, Communications and Applications, 2006. Google ScholarDigital Library
- A. W. M. Smeulders, M. Worring, S. Santini,A. Gupta and R. Jain. Content based Image RetrievalGoogle Scholar
- At the End of the Early Years. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.Google Scholar
- J. Li and J. Z. Wang. Real-Time Computerized Annotation of Pictures. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008. Google ScholarDigital Library
- F. Monay and D. G. Perez. On Image Auto-annotation with Latent Space Modeling. In Proceeding of 10th ACM International Conference on Multimedia, 2003. Google ScholarDigital Library
- G. Sychay, E. Y. Chang and K. Goh. Effective Image Annotation via Active Learning. In IEEE International Conference on Multimedia and Expo, 2002.Google ScholarCross Ref
- R. Shi, C. H. Lee and T. S. Chua. Enhancing Image Annotation by Integrating Concept Ontology and Text-based Bayesian Learning Model. In Proceeding of 14th ACM International Conference on Multimedia, 2007. Google ScholarDigital Library
- B. Sigurbjornsson and R. V. Zwol. Flickr Tag Recommendation based on Collective Knowledge. In Proceeding of ACM Interenational World Wide Web Conference, 2008. Google ScholarDigital Library
- X. R. Li, C. G. M. Snoek and M. Worring. Learning Tag Relevance by Neighbor Voting for Social Image Retrieval. In Proceedings of the ACM International Conference on Multimedia Information Retrieval, 2008. Google ScholarDigital Library
- R. Yan, A. Natsev and M. Campbell. A Learning-based Hybrid Tagging and Browsing Approach for Efficient Manual Image Annotation. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 2008.Google Scholar
- L. Wu, L. J. Yang, N. H. Yu and X. S. Hua. Learning to Tag. In Proceeding of ACM Interenational World Wide Web Conference, 2009. Google ScholarDigital Library
- H. M. Chen, M. H. Chang, P. C. Chang, M. C. Tien, W. H. Hsu and J. L. Wu. SheepDog-Group and Tag Recommendation for Flickr Photos by Automatic Search-based Learning. In Proceeding of 15th ACM International Conference on Multimedia, 2008. Google ScholarDigital Library
- E. Parzen. On the Estimation of a Probability Density Function and the Mode. In Annals of Mathematical Statistics, 1962.Google ScholarCross Ref
- B. Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, December, 2006. Google ScholarDigital Library
- R. Cilibrasi and P. M. B. Vitanyi. The Google Similarity Distance. In IEEE Transactions on Knowledge and Data Engineering, 2007. Google ScholarDigital Library
- W. H. Hsu, L. S. Kennedy and S. F. Chang. Video Search Reranking through Random Walk over Document-Level Context Graph. In Proceeding of 14th ACM International Conference on Multimedia, 2007. Google ScholarDigital Library
- Y. S. Jing and S. Baluja. VisualRank: Applying PageRank to Large-Scale Image Search. In Transactions on Pattern Analysis and Machine Intelligence, 2008. Google ScholarDigital Library
- J. Y. Pan, H. J. Yang, C. Faloutsos and P. Duygulu. Gcap: Graph-based Automatic Image Captioning. In International Workshop on Multimedia and Document Engineering, 2004.Google Scholar
- K. Jarvelin and J. Kekalainen. Cumulated Gain-Based Evaluation of IR Techniques. In ACM Transcations on Information System, 2002. Google ScholarDigital Library
Index Terms
- Tag ranking
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