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User-Perceptive Multimedia Content Analysis

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User-centric Social Multimedia Computing

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Typical social multimedia services allow users as uploaders, viewers, taggers, and commenters to interact and collaborate with each other in a communication dialog. The wisdom of crowds provides a huge resource for understanding social multimedia content. In this chapter, we explicitly model user interaction in the tag generation process and propose a regularized tensor factorization solution to refine the ternary correlations among user, image, and tag. While the traditional social tag analysis work focus on analyzing the image-tag binary correlation, taking user factor into consideration shows superior performance in image tag refinement task.

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Notes

  1. 1.

     We show a running example consisting of three users, five tags, and four images in Fig. 2.1a.

  2. 2.

     Note in most tag processing work, while tag is contributed by users, user factor is not explicitly considered. We will discuss the difference between our work in this chapter and the existing tag process work in next subsection.

  3. 3.

     In practice, for new images not in the training dataset, we can approximate their positions in the learnt image subspace by using approximated eigenfunctions based on the kernel trick [2].

  4. 4.

     We call triplets like \((u_3, i_2, :)\) and \((u_3, i_4, :)\) as the neutral triplets.

  5. 5.

     Detail of \(W^T\) construction is introduced in next subsection.

  6. 6.

     In the experiment, we choose \(\lambda _c=0.9\) and \(\lambda _s=0.1\).

  7. 7.

     The user factor \(U\) and tag factor\(T\) are the same cases as the image factor \(I\).

  8. 8.

     Due to link failures, the owner ID of some images is unavailable.

References

  1. Acar, E., Yener, B.: Unsupervised multiway data analysis: a literature survey. IEEE Trans. Knowl. Data Eng. 21(1), 6–20 (2009)

    Article  Google Scholar 

  2. Bengio, Y., Paiement, J.-F., Vincent, P., Delalleau, O., Roux, N.L., Ouimet, M.: Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In: NIPS (2003)

    Google Scholar 

  3. Borghol, Y., Ardon, S., Carlsson, N., Eager, D., Mahanti, A.: The untold story of the clones: content-agnostic factors that impact youtube video popularity. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’12, pp. 1186–1194 (2012)

    Google Scholar 

  4. Chen, L., Xu, D., Tsang, I.W.-H., Luo, J.: Tag-based web photo retrieval improved by batch mode re-tagging. In: CVPR, pp. 3440–3446 (2010)

    Google Scholar 

  5. Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: Nus-wide: a real-world web image database from national university of singapore. In: CIVR (2009)

    Google Scholar 

  6. Cranshaw, J., Schwartz, R., Hong, J.I., Sadeh, N.M.: The livehoods project: utilizing social media to understand the dynamics of a city. In: ICWSM (2012)

    Google Scholar 

  7. De Choudhury, M., Sundaram, H., John, A., Seligmann, D.D.: What makes conversations interesting? Themes, participants and consequences of conversations in online social media. In: Proceedings of the 18th International Conference on World Wide Web, WWW’09, pp. 331–340 (2009)

    Google Scholar 

  8. Eickhoff, C., Li, W., de Vries, A.P.: Exploiting user comments for audio-visual content indexing and retrieval. In: 34th European Conference on Information Retrieval (ECIR) (2013)

    Google Scholar 

  9. Fang, Q., Sang, J., Xu, C., Rui, Y.: Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Trans. Multimed. 16(3), 796–812 (2014)

    Article  Google Scholar 

  10. Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: KDD, pp. 1276–1284 (2012)

    Google Scholar 

  11. Filippova, K., Hall, K.B.: Improved video categorization from text metadata and user comments. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’11, pp. 835–842 (2011)

    Google Scholar 

  12. He, X., Kan, M.-Y., Xie, P., Chen, X.: Comment-based multi-view clustering of web 2.0 items. In: Proceedings of the 23rd International Conference on World Wide Web, WWW’14, pp. 771–782 (2014)

    Google Scholar 

  13. Helic, D.,Strohmaier, M.: Building directories for social tagging systems. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM’10, pp. 525–534 (2011)

    Google Scholar 

  14. Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: WSDM, pp. 537–546 (2013)

    Google Scholar 

  15. Jin, X., Wang, C., Luo, J., Yu, X., Han, J.: Likeminer: a system for mining the power of ‘like’ in social media networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’11, pp. 753–756 (2011)

    Google Scholar 

  16. Jin, Y., Khan, L., Wang, L., Awad, M.: Image annotations by combining multiple evidence & wordnet. In: ACM Multimedia, pp. 706–715 (2005)

    Google Scholar 

  17. Lappas, T., Punera, K., Sarlos, T.: Mining tags using social endorsement networks. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’11, pp. 195–204 (2011)

    Google Scholar 

  18. Li, W.-J., Yeung, D.-Y.: Relation regularized matrix factorization. In: IJCAI, pp. 1126–1131 (2009)

    Google Scholar 

  19. Li, Z., Liu, J., Zhu, X., Liu, T., Lu, H.: Image annotation using multi-correlation probabilistic matrix factorization. In: ACM Multimedia, pp. 1187–1190 (2010)

    Google Scholar 

  20. Liu, D., Hua, X.-S., Wang, M., Zhang, H.-J.: Image retagging. In: ACM Multimedia, pp. 491–500 (2010)

    Google Scholar 

  21. Liu, D., Hua, X.-S., Yang, L., Wang, M., Zhang, H.-J.: Tag ranking. In: WWW, pp. 351–360 (2009)

    Google Scholar 

  22. Liu, D., Hua, X.-S., Zhang, H.-J.: Content-based tag processing for internet social images. Multimed. Tool. Appl. 51, 723–738 (2011)

    Article  Google Scholar 

  23. Liu, D., Yan, S., Rui, Y., Zhang, H.-J.: Unified tag analysis with multi-edge graph. In: ACM Multimedia, pp. 25–34 (2010)

    Google Scholar 

  24. Liu, J., Wang, B., Li, M., Li, Z., Ma, W.-Y., Lu, H., Ma, S.: Dual cross-media relevance model for image annotation. In: ACM Multimedia, pp. 605–614 (2007)

    Google Scholar 

  25. Liu, X., Yan, S., Cheng, B., Tang, J., Chua, T.-S., Jin, H.: Label-to-region with continuity-biased bi-layer sparsity priors. ACM Trans. Multimed. Comput. Commun. Appl. (TOMCCAP) 8(4), 50 (2012)

    Google Scholar 

  26. Lu, C., Hu, X., Chen, X., Park, J.-R., He, T., Li, Z.: The topic-perspective model for social tagging systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 683–692 (2010)

    Google Scholar 

  27. man Au Yeung, C., Gibbins, N., Shadbolt, N.: A study of user profile generation from folksonomies. In: SWKM (2008)

    Google Scholar 

  28. Pinto, H., Almeida, J.M., Gonçalves, M.A.: Using early view patterns to predict the popularity of youtube videos. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM’13, pp. 365–374 (2013)

    Google Scholar 

  29. Plangprasopchok, A., Lerman, K., Getoor, L.: Growing a tree in the forest: Constructing folksonomies by integrating structured metadata. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’10, pp. 949–958 (2010)

    Google Scholar 

  30. Potthast, M., Stein, B., Becker, S.: Towards comment-based cross-media retrieval. In: Proceedings of the 19th International Conference on World Wide Web, WWW’10, pp. 1169–1170 (2010)

    Google Scholar 

  31. Rendle, S., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: KDD, pp. 727–736 (2009)

    Google Scholar 

  32. Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM, pp. 81–90 (2010)

    Google Scholar 

  33. Sang, J., Liu, J., Xu, C.: Exploiting user information for image tag refinement. In: ACM Multimedia, pp. 1129–1132 (2011)

    Google Scholar 

  34. Sang, J., Xu, C., Liu, J.: User-aware image tag refinement via ternary semantic analysis. IEEE Trans. Multimed. 14(3–2), 883–895 (2012)

    Article  Google Scholar 

  35. Sang, J., Xu, C., Lu, D.: Learn to personalized image search from the photo sharing websites. IEEE Trans. Multimed. 14(4), 963–974 (2012)

    Article  Google Scholar 

  36. Siersdorfer, S., Chelaru, S., Nejdl, W., San Pedro, J.: How useful are your comments? Analyzing and predicting youtube comments and comment ratings. In: Proceedings of the 19th International Conference on World Wide Web, WWW’10, pp. 891–900 (2010)

    Google Scholar 

  37. Trevisiol, M., Jégou, H., Delhumeau, J., Gravier, G.: Retrieving geo-location of videos with a divide & conquer hierarchical multimodal approach. In: ICMR, pp. 1–8 (2013)

    Google Scholar 

  38. von Ahn, L., Dabbish, L.: Esp: Labeling images with a computer game. In: AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors, pp. 91–98 (2005)

    Google Scholar 

  39. 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 

  40. Wang, C., Jing, F., Zhang, L., Zhang, H.-J.: Content-based image annotation refinement. In: CVPR (2007)

    Google Scholar 

  41. Xie, L., Natsev, A., Hill, M.L., Smith, J.R., Phillips, A.: The accuracy and value of machine-generated image tags: design and user evaluation of an end-to-end image tagging system. In: CIVR, pp. 58–65 (2010)

    Google Scholar 

  42. Xu, H., Wang, J., Hua, X.-S., Li, S.: Tag refinement by regularized lda. In: ACM Multimedia, pp. 573–576 (2009)

    Google Scholar 

  43. Yamamoto, T., Nakamura, S.: Leveraging viewer comments for mood classification of music video clips. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’13, pp. 797–800 (2013)

    Google Scholar 

  44. Ye, M., Shou, D., Lee, W.-C., Yin, P., Janowicz, K.: On the semantic annotation of places in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’11, pp. 520–528 (2011)

    Google Scholar 

  45. Yu, B., Ma, W.-Y., Nahrstedt, K., Zhang, H.-J.: Video summarization based on user log enhanced link analysis. In: Proceedings of the Eleventh ACM International Conference on Multimedia, MULTIMEDIA’03, pp. 382–391 (2003)

    Google Scholar 

  46. Zhou, Y., Wilkinson, D.M., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: AAIM, pp. 337–348 (2008)

    Google Scholar 

  47. Zhu, G., Yan, S., Ma, Y.: Image tag refinement towards low-rank, content-tag prior and error sparsity. In: ACM Multimedia, pp. 461–470 (2010)

    Google Scholar 

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Authors and Affiliations

  1. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jitao Sang

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Correspondence to Jitao Sang .

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Sang, J. (2014). User-Perceptive Multimedia Content Analysis. In: User-centric Social Multimedia Computing. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44671-3_2

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  • DOI: https://doi.org/10.1007/978-3-662-44671-3_2

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