Tag refinement by regularized LDA

ABSTRACT

Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many tags are irrelevant to image content. To refine the tags, previous solutions usually mine tag relevance relying on the tag similarity estimated right from the corpus to be refined. The calculation of tag similarity is affected by the noisy tags in the corpus, which is not conducive to estimate accurate tag relevance. In this paper, we propose to do tag refinement from the angle of topic modeling. In the proposed scheme, tag similarity and tag relevance are jointly estimated in an iterative manner, so that they can benefit from each other. Specifically, a novel graphical model, regularized Latent Dirichlet Allocation (rLDA), is presented. It facilitates the topic modeling by exploiting both the statistics of tags and visual affinities of images in the corpus. The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method.

REFERENCES

• 1: D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, 2003.
• 2: S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American society for information science, 41(6):391--407, 1990.
• 3: T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd international ACM SIGIR conference on Research and development in information retrieval, pages 50--57, Berkeley, United States, 1999.
• 4: J. Jia, N. Yu, X. Rui, and M. Li. Multi-graph similarity reinforcement for image annotation refinement. In 15th IEEE International Conference on Image Processing, 2008.
• 5: ICIP 2008, pages 993--996, 2008.
• 6: Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotations by combining multiple evidence & wordNet. In Proceedings of the 13th ACM international conference on Multimedia, pages 706--715. New York, USA, 2005.
• 7: X. 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, pages 180--187, 2008.
• 8: D. Liu, X. S. Hua, L. Yang, M. Wang, and H. J. Zhang. Tag ranking. In Proceedings of the 18th international conference on World wide web, pages 351--360. New York, USA, 2009.
• 9: C. Wang, F. Jing, L. Zhang, and H. Zhang. Content-based image annotation refinement. In Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, 2007.
• 10: C. Wang, F. Jing, L. Zhang, and H. J. Zhang. Image annotation refinement using random walk with restarts. In Proceedings of the 14th ACM international conference on Multimedia, pages 647--650. New York, USA, 2006.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Experimentation, Theory

Keywords:
regularized LDA, tag refinement, tag relevance