Context Sensitive Tag Expansion with Information Inference

  • Hongyun Cai
  • Zi Huang
  • Jie Shao
  • Xue Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7238)

Abstract

The exponential explosion of web image data on the Internet has been witnessed over the last few years. The precise labeling of these images is crucial to effective image retrieval. However, most existing image tagging methods discover the correlations from tag co-occurrence relationship, which leads to the limited scope of extended tags. In this paper, we study how to build a new information inference model over image tag datasets for more effective and complete tag expansion. Specifically, the proposed approach uses modified Hyperspace Analogue to Language (HAL) model instead of association rules or latent dirichlet allocations to mine the correlations between image tags. It takes advantage of context sensitive information inference to overcome the limitation caused by the tag co-occurrence based methods. The strength of this approach lies in its ability to generate additional tags that are relevant to a target image but may have weak co-occurrence relationship with the existing tags in the target image. We demonstrate the effectiveness of this proposal with extensive experiments on a large Flickr image dataset.”

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Bai, J., Song, D., Bruza, P., Nie, J.-Y., Cao, G.: Query expansion using term relationships in language models for information retrieval. In: CIKM, pp. 688–695 (2005)Google Scholar
  5. 5.
    Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press (1997)Google Scholar
  6. 6.
    Burgess, C., Livesay, K., Lund, K.: Explorations in context space: Words, sentences, discourse. Discourse Processes 25(2/3), 211–257 (1998)CrossRefGoogle Scholar
  7. 7.
    Burgess, C., Lund, K.: Modeling parsing constraints with high-dimensional context space. Language and Cognitive Processes 12(2/3), 177–210 (1997)CrossRefGoogle Scholar
  8. 8.
    Datta, R., Ge, W., Li, J., Wang, J.Z.: Toward bridging the annotation-retrieval gap in image search. IEEE MultiMedia 14(3), 24–35 (2007)CrossRefGoogle Scholar
  9. 9.
    Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought. MIT Press (2000)Google Scholar
  10. 10.
    Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Information Science 32(2), 198–208 (2006)CrossRefGoogle Scholar
  11. 11.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: SIGIR, pp. 531–538 (2008)Google Scholar
  12. 12.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the 2009 ACM Conference on Recommender Systems, pp. 61–68 (2009)Google Scholar
  13. 13.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Proceedings of the 17th ACM Conference on Hypertext and Hypermedia, pp. 31–40 (2006)Google Scholar
  14. 14.
    Moxley, E., Mei, T., Manjunath, B.S.: Video annotation through search and graph reinforcement mining. IEEE Transactions on Multimedia 12(3), 184–193 (2010)CrossRefGoogle Scholar
  15. 15.
    Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: WWW, pp. 327–336 (2008)Google Scholar
  16. 16.
    Song, D., Bruza, P.: Towards context sensitive information inference. JASIST 54(4), 321–334 (2003)CrossRefGoogle Scholar
  17. 17.
    Song, D., Bruza, P., Cole, R.: Concept learning and information inferencing on a high dimensional semantic space. In: Proceedings ACM SIGIR 2004 Workshop on Mathematical/Formal Methods in Information Retrieval (2004)Google Scholar
  18. 18.
    Wang, C., Jing, F., Zhang, L., Zhang, H.: Image annotation refinement using random walk with restarts. In: ACM Multimedia, pp. 647–650 (2006)Google Scholar
  19. 19.
    Wu, L., Yang, L., Yu, N., Hua, X.-S.: Learning to tag. In: WWW, pp. 361–370 (2009)Google Scholar
  20. 20.
    Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: Collaborative tag suggestions. In: Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006 (2006)Google Scholar
  21. 21.
    Yang, Y., Huang, Z., Shen, H.T., Zhou, X.: Mining multi-tag association for image tagging. World Wide Web 14(2), 133–156 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hongyun Cai
    • 1
  • Zi Huang
    • 1
  • Jie Shao
    • 1
  • Xue Li
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandAustralia

Personalised recommendations