Advertisement

Computing Tag-Diversity for Social Image Search

  • Eunggyo Kim
  • Takehiro Yamamoto
  • Katsumi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8839)

Abstract

“Image search” on the basis of social tags is now a popular tool provided by image-sharing services. Some images are annotated with similar tags, while others are annotated with dissimilar ones. In this study, a concept called “tag-diversity,” which represents how diverse tags are annotated to an image, is proposed, and two methods to estimate it are proposed. We conducted the experiment to investigate how the two proposed methods accurately compute tag-diversity. The results of the experiment show that both methods outperformed the baseline method, which calculates tag-diversity on the basis of the number of annotated tags. We also show some images with low and high tag-diversity, and discuss how tag-diversity can improve the current image search.

Keywords

Image Retrieval Social Tagging Diversity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV (2004)Google Scholar
  2. 2.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: IEEE Computer Vision and Pattern Recognition (2009)Google Scholar
  3. 3.
    Guo, J., Cheng, X., Xu, G., Shen, H.: A Structured Approach to Query Recommendation with Social Annotation Data. In: Proc. of CIKM2010, pp. 619–628 (2010)Google Scholar
  4. 4.
    Heymann, P., Koutrika, G., Garcia-Molina, H.: Can social bookmarking improve web search? In: Proc. of WSDM 2008, pp. 195–206 (2008)Google Scholar
  5. 5.
    Kato, M., Ohshima, H., Oyama, S., Tanaka, K.: Can social tagging improve web image search? In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 235–249. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Makadia, A., Pavlovic, V., Kumar, S.: A new baseline for image annotation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 316–329. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proc. of RecSys 2008, pp. 259–266 (2008)Google Scholar
  8. 8.
    Stirling, A.: A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface 4(15), 707–719 (2007)CrossRefGoogle Scholar
  9. 9.
    Sumida, A., Yoshinaga, N., Torisawa, K.: Boosting precision and recall of hyponymy relation acquisition from hierarchical layouts in wikipedia. In: Proc. of International Language Resources and Evaluation, pp. 2462–2469 (2008)Google Scholar
  10. 10.
    Yanbe, Y., Jatowt, A., Nakamura, S., Tanaka, K.: Can social bookmarking enhance search in the web? In: Proc. of JCDL 2007, pp. 107–116 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eunggyo Kim
    • 1
  • Takehiro Yamamoto
    • 1
  • Katsumi Tanaka
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversitySakyoJapan

Personalised recommendations