Skip to main content
SpringerLink
Log in

Tag ranking based on salient region graph propagation

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

In this paper, the problem of tag ranking by propagating relevance over community-contributed images and their associated tags is explored. To rank the tags more accurately, we propose a novel tag ranking approach based on the salient region. Firstly, we extract the salient region sub-image based on Itti model, and then construct two graphs with the whole image and the salient region sub-image. Secondly, we use the graph-based model to propagate the relevance of labels. Finally, we calculate the relevance score according to the results of the relevance propagation on the two sparse graphs. And then the new order of tags is determined by the relevance of the tags from high to low. Compared to existing methods, the proposed method considers not only the relationship between the whole images, but also the relationship between the salient regions. Therefore, it enhances the accuracy of the tag ranking. Experimental results conducted on a real dataset demonstrate that the ranking result of the proposed approach is closer to the manual rank.

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. Tang, J., Yan, S., et al.: Inferring semantic concepts from community-contributed images and noisy tags. In: ACM International Conference on Multimedia, pp. 223–232 (2009)

  2. Xiang, Y., Zhou, X., Chua, T.-S., et al.: A revisit of generative model for automatic image annotation using Markov random fields. In: IEEE CVPR, pp. 1153–1160 (2009)

  3. Zhang, S., Tian, Q., et al.: Descriptive visual words and visual phrases for image applications. In: ACM Multimedia, pp. 75–84 (2009)

  4. Xiang, Y., Zhou, X., et al.: Semantic context modeling with maximal margin conditional random fields for automatic image annotation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3368–3375 (2010)

  5. Yang, Y., Huang, Z., Shen, H.T., Zhou, X.: Mining multi-tag association for image tagging. World Wide Web 14(2), 133–156 (2011)

    Article  Google Scholar 

  6. Yang, Y., Yang, Y., Huang, Z., Shen H.T., Nie, F.: Tag localization with spatial correlations and joint group sparsity. In: IEEE CVPR, pp. 881–888 (2011)

  7. Liu, D., Hua, X.S., Yang, L.J., et al.: Tag ranking. In: 18th international conference on World Wide Web, pp. 351–360 (2009)

  8. Li, X.R., Snoek, C.G.M., Worring, M.: Learning tag relevance by neighbor voting for social image retrieval. In: ACM ICMR, pp. 180–187 (2008)

  9. Zhuang, J., Hoi, S.C.H.: A two-view learning approach for image tag ranking. In: ACM International Conference on Web Search and Data Mining, pp. 625–634 (2011)

  10. Zha, Z.J., Mei, T., Wang, J., Wang, Z., Hua, X.S.: Graph-based semi-supervised learning with multiple labels. J. Vis. Commun. Image Represent. 20(2), 97–103 (2009)

    Article  Google Scholar 

  11. Feng, S., Lang, C., Xu, D.: Beyond tag relevance integrating visual attention model and multi-instance learning for tag saliency ranking. In: ACM ICMR, pp. 288–295 (2010)

  12. Li, M.X, Tang, J.H., Li, H.J., Zhao, C.X.: Tag ranking by propagating relevance over tag and image graphs. In: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, pp. 153–156 (2012)

  13. Zha, Z.J., Wang, M., Zheng, Y., Yang, Y., Hong, R., Chua, T.S.: Interactive video indexing with statistical active learning. IEEE Trans. Multimed. 14(1), 17–27 (2012)

    Article  Google Scholar 

  14. Zha, Z.J., Yang, L., Mei, T., Wang, M., Wang, Z.: Visual query suggestion. In: ACM Multimedia, pp. 15–24 (2009)

  15. Zha, Z.J., Hua, X.S., Mei, T., Wang, J., Qi, G.J., Wang Z.: Joint multi-label multi-instance learning for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

  16. Yang, Y., Yang, Y., Shen, H.T.: Effective transfer tagging from image to video. In: ACM Transactions on Multimedia Computing, Communications and Applications (2013)

  17. Li, Z.C., Liu, J., Zhu, X.B., Liu, T.L., Lu, H.Q.: Image Annotation using multi-correlation probabilistic matrix factorization. In: ACM International Conference on Multimedia (2010)

  18. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  19. Rao, R., Olshausen, B., Lewicki, M.: Probabilistic models of the brain: perception and neural function. MIT Press, Cambridge (2001)

    Google Scholar 

  20. Tang, J., Hong, R., Yan, S., et al.: Image annotation by kNN-sparse graph-based label propagation over noisily-tagged web images. ACM Trans. Intell. Syst. Technol. 2(2), 111–126 (2011)

    Article  Google Scholar 

  21. Li, Z.C., Yang, Y., Liu, J., Zhou, X.F., Lu, H.Q.: Unsupervised feature selection using nonnegative spectral analysis. In: AAAI (2012)

  22. Donoho, D.L.: For most large underdetermined systems of linear equations the minimal l 1 l 1 -norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  23. L1-Magic, http://users.ece.gatech.edu/~justin/l1magic/. Accessed 5 Jan 2013

  24. Mount, D., Araya, S. Ann: A library for approximate nearest neighbor searching. In: CGC 2nd Annual Fall Workshop on Computational Geometry (1997)

  25. Anderson, J.R.: The architecture of cognition. Harvard University Press, Cambridge (1983)

    Google Scholar 

  26. Shrager, J., Hogg, T., Huberman, B.A.: Observation of phase transitions in spreading activation networks. Science 236(4805), 1092–1094 (1987)

    Article  Google Scholar 

  27. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Advances in neural information processing systems. MIT Press, Cambridge (2001)

    Google Scholar 

  28. Chua, T.-S., Tang, J., et al.: NUS-WIDE: a real-world web image database from National University of Singapore. In: ACM Conference on Image and Video Retrieval (2009)

  29. Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, USA (2003)

    Google Scholar 

  30. Huang, J., Kumar, S.R., Mitra, M., et al.: Image indexing using color correlogram. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)

  31. Park, D.K., Jeon, Y.S., Won, C.S.: Efficient use of local edge histogram descriptor. In: ACM Workshops on Multimedia, pp. 51–54 (2000)

  32. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

  33. Stricker, M., Orengo, M. Similarity of color images. In: SPIE Storage and Retrieval for Image and Video Databases III, pp. 381–392 (1995)

  34. Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

    Jinhui Tang, Minxian Li, Zechao Li & Chunxia Zhao

Authors
  1. Jinhui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minxian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zechao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunxia Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minxian Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tang, J., Li, M., Li, Z. et al. Tag ranking based on salient region graph propagation. Multimedia Systems 21, 267–275 (2015). https://doi.org/10.1007/s00530-014-0357-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-014-0357-1

Keywords

Search

Navigation