Skip to main content
SpringerLink
Log in

Interactive browsing via diversified visual summarization for image search results

  • Interactive Multimedia Computing
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Presenting and browsing image search results play key roles in helping users to find desired images from search results. Most existing commercial image search engines present them, dependent on a ranked list. However, such a scheme suffers from at least two drawbacks: inconvenience for consumers to get an overview of the whole result, and high computation cost to find desired images from the list. In this paper, we introduce a novel search result summarization approach and exploit this approach to further propose an interactive browsing scheme. The main contribution of this paper includes: (1) a dynamic absorbing random walk to find diversified representatives for image search result summarization; (2) a local scaled visual similarity evaluation scheme between two images through inspecting the relation between each image and other images; and (3) an interactive browsing scheme, based on a tree structure for organizing the images obtained from the summarization approach, to enable users to intuitively and conveniently browse the image search results. Quantitative experimental results and user study demonstrate the effectiveness of the proposed summarization and browsing approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Cai, D., He, X., Li, Z., Ma, W.-Y., Wen, J.-R.: Hierarchical clustering of WWW image search results using visual, textual and link information. In: ACM multimedia, pp. 952–959 (2004)

  2. Chang, C.-C., Lin, C.-J.: LIBSVM—a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (2009)

  3. Fan, J., Gao, Y., Luo, H., Keim, D.A., Li, Z.: A novel approach to enable semantic and visual image summarization for exploratory image search. In: Multimedia information retrieval, pp. 358–365 (2008)

  4. Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78, 553–569 (1983)

    Article  MATH  Google Scholar 

  5. Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)

    Article  MathSciNet  Google Scholar 

  6. Gao, B., Liu, T.-Y., Qin, T., Zheng, X., Cheng, Q., Ma, W.-Y.: Web image clustering by consistent utilization of visual features and surrounding texts. In: ACM multimedia, pp. 112–121 (2005)

  7. Jia, Y., Wang, J., Zhang, C., Hua, X.-S.: Finding image exemplars using fast sparse affinity propagation. In: ACM multimedia, pp. 639–642 (2008)

  8. Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., Ma, W.-Y. (2006) Igroup: web image search results clustering. In: ACM multimedia, pp. 377–384

  9. Jing, Y., Rowley, H.A., Rosenberg, C., Wang, J., Covell, M.: Visualizing Web images via Google Image Swirl. In: NIPS workshop on statistical machine learning for visual analytics (2009)

  10. Liu, H., Xie, X., Tang, X., Li, Z., Ma, W.-Y.: Effective browsing of web image search results. In: Multimedia information retrieval, pp. 84–90 (2004)

  11. Mei, T., Hua, X.-S., Zhu, C.-Z., Zhou, H.-Q., Li, S.: Home video visual quality assessment with spatiotemporal factors. IEEE Trans. Circuits Syst. Video Technol. 17(6), 699–706 (2007)

    Article  Google Scholar 

  12. Meilă, M.: Comparing clusterings—an information based distance. J. Multivar. Anal. 98(5), 873–895 (2007)

    Article  MATH  Google Scholar 

  13. Moëllic, P.-A., Haugeard, J.-E., Pitel, G.: Image clustering based on a shared nearest neighbors approach for tagged collections. In: CIVR, pp. 269–278 (2008)

  14. Song, K., Tian, Y., Gao, W., Huang, T.: Diversifying the image retrieval results. In: ACM multimedia, pp. 707–710 (2006)

  15. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 5500, 2319–2323 (2000)

    Article  Google Scholar 

  16. van Leuken, R.H., Garcia, L., Olivares, X., van Zwol, R.: Visual diversification of image search results. In: WWW, pp. 341–350 (2009)

  17. van Zwol, R., Murdock, V., Pueyo, L.G., Ramírez, G.: Diversifying image search with user generated content. In: Multimedia information retrieval, pp. 67–74 (2008)

  18. Wang, J., Wang, F., Zhang, C., Shen, H.C., Quan, L.: Linear neighborhood propagation and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1600–1615 (2009)

    Article  Google Scholar 

  19. Wang, J., Zhao, Y., Wu, X., Hua, X.-S.: Transductive multi-label learning for video concept detection. In: Multimedia information retrieval, pp. 298–304 (2008)

  20. Wang, J., Zhao, Y., Wu, X., Hua, X.-S.: A transductive multi-label learning approach for video concept detection. Pattern Recogn., to appear (2010)

  21. Wang, M., Hua, X.-S., Hong, R., Tang, J., Qi, G.-J., Song, Y.: Unified video annotation via multi-graph learning. IEEE Trans. Circuits Syst. Video Technol. 19(5), 733–746 (2009)

    Article  Google Scholar 

  22. Wang, M., Hua, X.-S., Tang, J., Hong, R.: Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Trans. Multimed. 11(3), 465–476 (2009)

    Article  Google Scholar 

  23. Weinberger, K.Q., Slaney, M., van Zwol, R.: Resolving tag ambiguity. In: ACM multimedia, pp. 111–120 (2008)

  24. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: NIPS (2004)

  25. Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., Ma, W.-Y.: Improving web search results using affinity graph. In: SIGIR, pp. 504–511 (2005)

  26. Zhu, X., Goldberg, A., Gael, J.V., Andrzejewski, D.: Improving diversity in ranking using absorbing random walks. In: Proceedings of the annual conference of the North American chapter of the association for computational linguistics (NAACL-HLT) (2007)

Download references

Author information

Authors and Affiliations

  1. Beijing, China

    Jingdong Wang, Liyan Jia & Xian-Sheng Hua

Authors
  1. Jingdong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liyan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xian-Sheng Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingdong Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, J., Jia, L. & Hua, XS. Interactive browsing via diversified visual summarization for image search results. Multimedia Systems 17, 379–391 (2011). https://doi.org/10.1007/s00530-010-0224-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-010-0224-7

Keywords

Search

Navigation