Skip to main content
SpringerLink
Log in

Content selection criteria for news multi-video summarization based on human strategies

  • Published:
International Journal on Digital Libraries Aims and scope Submit manuscript

Abstract

In the recent years, the multimedia data volume produced and available for access has increased continuously and quickly, notably video content. This context has also increased the overload information problem: finding content of interest in the huge amount of available options. So, efficient schemes for content access are needed. Automatic video summarization is a research field that deals with this problem. Furthermore, the current multimedia systems make available several videos related to the same topic but having, each one, a piece of unique complementary information. This fact highlights the need for multi-video summarization to deal with users’ interest in being informed about a subject from a set of videos without being obligated to watch the whole set. However, the literature analysis shows that human strategies are not considered to define criteria used to automatically select video segments that will compose a summary and the focus of techniques has been the identification of common information in different videos. In this work, we investigate human strategies for news multi-video summarization. The results of the study with real users uncover relevant criteria to develop summaries, with potential to increase their semantics and bring them closer to users’ perception.

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

Similar content being viewed by others

References

  1. Almeida, J., Leite, N.J., da Silva Torres, R.: Online video summarization on compressed domain. J. Vis. Commun. Image Represent. 24(6), 729–738 (2013). https://doi.org/10.1016/j.jvcir.2012.01.009

    Article  Google Scholar 

  2. Camargo, R.T., Di-Felippo, A., Pardo, T.A.S.: On strategies of human multi-document summarization. In: Proceedings of Symposium in Information and Human Language Technology, pp. 141–150. Sociedade Brasileira de Computação, Natal (2015)

  3. Chen, F., De Vleeschouwer, C., Cavallaro, A.: Resource allocation for personalized video summarization. IEEE Trans. Multimed. 16(2), 455–469 (2014). https://doi.org/10.1109/TMM.2013.2291967

    Article  Google Scholar 

  4. Cisco: white paper: Cisco VNI forecast and methodology, 2015–2020 (2016). http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html. Accessed 01 June 2017

  5. Dale, K., Shechtman, E., Avidan, S., Pfister, H.: Multi-video browsing and summarization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2012). https://doi.org/10.1109/CVPRW.2012.6239253

  6. Groupon: why video should be a part of your 2015 marketing strategy (2014). https://www.groupon.co.uk/merchant/blog/why-video-should-be-a-part-of-your-2014-marketing-strategy. Accessed 01 June 2017

  7. Gygli, M., Grabner, H., Riemenschneider, H., Van Gool, L.: Creating summaries from user videos. In: Computer Vision—ECCV 2014, Lecture Notes in Computer Science, vol. 8695, pp. 505–520. Springer International Publishing (2014). https://doi.org/10.1007/978-3-319-10584-0_33

  8. Hong, R., Tang, J., Tan, H.K., Yan, S., Ngo, C., Chua, T.S.: Event driven summarization for web videos. In: Proceedings of the first SIGMM workshop on social media, WSM ’09, pp. 43–48. ACM, New York (2009). https://doi.org/10.1145/1631144.1631154

  9. Ide, I., Zhang, Y., Tanishige, R., Doman, K., Kawanishi, Y., Deguchi, D., Murase, H.: Summarization of news videos considering the consistency of auditory and visual contents. In: 2017 IEEE International Symposium on Multimedia (ISM), pp. 193–199 (2017). https://doi.org/10.1109/ISM.2017.33

  10. Ji, Z., Zhang, Y., Pang, Y., Li, X.: Hypergraph dominant set based multi-video summarization. Signal Process. 148(C), 114–123 (2018). https://doi.org/10.1016/j.sigpro.2018.01.028

    Article  Google Scholar 

  11. Kanehira, A., Van Gool, L., Ushiku, Y., Harada, T.: Viewpoint-aware video summarization. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7435–7444 (2018). https://doi.org/10.1109/CVPR.2018.00776

  12. Kannan, R., Ghinea, G., Swaminathan, S., Kannaiyan, S.: Improving video summarization based on user preferences. In: Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4 (2013). https://doi.org/10.1109/NCVPRIPG.2013.6776187

  13. Li, Y., Merialdo, B.: Multi-video summarization based on AV-MMR. In: International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–6 (2010). https://doi.org/10.1109/CBMI.2010.5529899

  14. Li, Y., Merialdo, B.: Multi-video summarization based on video-MMR. In: 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, pp. 1–4 (2010)

  15. Li, Y., Merialdo, B.: Multi-video summarization based on OB-MMR. In: 9th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 163–168 (2011). https://doi.org/10.1109/CBMI.2011.5972539

  16. Li, Y., Merialdo, B.: Video Summarization Based on Balanced AV-MMR, pp. 370–382. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-27355-1_35

  17. Li, Y., Merialdo, B.: Multimedia maximal marginal relevance for multi-video summarization. Multimed Tools Appl. 75(1), 199–220 (2016). https://doi.org/10.1007/s11042-014-2287-5

    Article  Google Scholar 

  18. Li, Y., Merialdo, B., Rouvier, M., Linares, G.: Static and dynamic video summaries. In: Proceedings of the 19th ACM International Conference on Multimedia, MM ’11, pp. 1573–1576. ACM, New York (2011). https://doi.org/10.1145/2072298.2072068

  19. Netflix: About Netflix - Netflix has been leading the way for digital content since 1997 (2019). https://media.netflix.com/en/about-netflix. Accessed 30 Apr 2019

  20. Nie, L., Hong, R., Zhang, L., Xia, Y., Tao, D., Sebe, N.: Perceptual attributes optimization for multivideo summarization. IEEE Trans. Cybern. 46(12), 2991–3003 (2016). https://doi.org/10.1109/TCYB.2015.2493558

    Article  Google Scholar 

  21. Nielsen, J.: how many test users in a usability study? (2012). https://www.nngroup.com/articles/how-many-test-users/. Accessed 12 Sep 2019

  22. Ou, S.H., Lee, C.H., Somayazulu, V.S., Chen, Y.K., Chien, S.Y.: On-line multi-view video summarization for wireless video sensor network. IEEE J. Sel. Top. Signal Process. 9(1), 165–179 (2015). https://doi.org/10.1109/JSTSP.2014.2331916

    Article  Google Scholar 

  23. Ou, S.H., Lu, Y.C., Wang, J.P., Chien, S.Y., Lin, S.D., Yeti, M.Y., Lee, C.H., Gibbons, P.B., Somayazulu, V.S., Chen, Y.K.: Communication-efficient multi-view keyframe extraction in distributed video sensors. In: IEEE Visual Communications and Image Processing Conference, pp. 13–16 (2014). https://doi.org/10.1109/VCIP.2014.7051492

  24. Panda, R., Mithun, N.C., Roy-Chowdhury, A.K.: Diversity-aware multi-video summarization. IEEE Trans. Image Process. 26(10), 4712–4724 (2017). https://doi.org/10.1109/TIP.2017.2708902

    Article  MathSciNet  Google Scholar 

  25. Panda, R., Roy-Chowdhury, A.K.: Collaborative summarization of topic-related videos. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4274–4283 (2017). https://doi.org/10.1109/CVPR.2017.455

  26. Petrie, H., Bevan, N.: The Evaluation of Accessibility, Usability, and User Experience, pp. 1–30. CRC Press, Boca Raton (2009)

    Google Scholar 

  27. Potapov, D., Douze, M., Harchaoui, Z., Schmid, C.: Category-Specific Video Summarization. In: Computer Vision – ECCV 2014, Lecture Notes in Computer Science, vol. 8694, pp. 540–555. Springer International Publishing (2014). https://doi.org/10.1007/978-3-319-10599-4_35

  28. Preece, J., Rogers, Y., Sharp, H.: Interaction Design: Beyond Human-Computer Interaction, 1st edn. John Wiley & Sons, New York (2002)

    Google Scholar 

  29. Rajendra, S.P., Keshaveni, N.: A survey of automatic video summarization techniques. Int. J. Electron. Electr. Comput. Syst. 3(1), 1–6 (2014)

    Article  Google Scholar 

  30. Sears, A., Hanson, V.L.: Representing users in accessibility research. ACM Trans. Access. Comput. 4(2), 7:1–7:6 (2012). https://doi.org/10.1145/2141943.2141945

    Article  Google Scholar 

  31. Shao, J., Jiang, D., Wang, M., Chen, H., Yao, L.: Multi-video summarization using complex graph clustering and mining. Comput. Sci. Inf. Syst. 7(1), 85–98 (2010)

    Article  Google Scholar 

  32. Toffler, A.: Future Shock, 1st edn. Bantam, New York (1984)

    Google Scholar 

  33. Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimed. Comput. Commun. Appl. 3(1), 3 (2007). https://doi.org/10.1145/1198302.1198305

    Article  Google Scholar 

  34. Wang, F., Merialdo, B.: Multi-document video summarization. In: IEEE International Conference on Multimedia and Expo, pp. 1326–1329 (2009). https://doi.org/10.1109/ICME.2009.5202747

  35. Wang, M., Hong, R., Li, G., Zha, Z.J., Yan, S., Chua, T.S.: Event driven web video summarization by tag localization and key-shot identification. IEEE Trans. Multimed. 14(4), 975–985 (2012). https://doi.org/10.1109/TMM.2012.2185041

    Article  Google Scholar 

  36. Yoshitaka, A., Sawada, K.: Personalized video summarization based on behavior of viewer. In: Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), pp. 661–667 (2012). https://doi.org/10.1109/SITIS.2012.100

  37. YouTube: statistics-global reach (2019). https://www.youtube.com/yt/press/statistics.html. Accessed 30 Apr 2019

  38. Zhang, L., Gao, Y., Hong, R., Hu, Y., Ji, R., Dai, Q.: Probabilistic skimlets fusion for summarizing multiple consumer landmark videos. IEEE Trans. Multimed. 17(1), 40–49 (2015). https://doi.org/10.1109/TMM.2014.2370257

    Article  Google Scholar 

  39. Zhang, L., Xia, Y., Mao, K., Ma, H., Shan, Z.: An effective video summarization framework toward handheld devices. IEEE Trans. Ind. Electron. 62(2), 1309–1316 (2015). https://doi.org/10.1109/TIE.2014.2336639

    Article  Google Scholar 

  40. Zhang, Y., Ma, C., Zhang, J., Zhang, D., Liu, Y.: An interactive personalized video summarization based on sketches. In: Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI ’13, pp. 249–258. ACM, New York (2013). https://doi.org/10.1145/2534329.2534343

  41. Zhang, Y., Ma, H., Zimmermann, R.: Dynamic multi-video summarization of sensor-rich videos in geo-space, pp. 380–390. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35725-1_35

  42. Zhang, Y., Wang, G., Seo, B., Zimmermann, R.: Multi-video summary and skim generation of sensor-rich videos in geo-space. In: Proceedings of the 3rd Multimedia Systems Conference, MMSys ’12, pp. 53–64. ACM, New York (2012). https://doi.org/10.1145/2155555.2155565

  43. Zhang, Y., Zhang, L., Zimmermann, R.: Aesthetics-guided summarization from multiple user generated videos. ACM Trans. Multimed. Comput. Commun. Appl. 11(2), 24:1–24:23 (2015). https://doi.org/10.1145/2659520

    Article  Google Scholar 

  44. Zhang, Y., Zimmermann, R.: Efficient summarization from multiple georeferenced user-generated videos. IEEE Trans. Multimed. 18(3), 418–431 (2016). https://doi.org/10.1109/TMM.2016.2520827

    Article  Google Scholar 

  45. Zhu, J., Liao, S., Li, S.Z.: Multicamera joint video synopsis. IEEE Trans. Circuits Syst. Video Technol. 26(6), 1058–1069 (2016). https://doi.org/10.1109/TCSVT.2015.2430692

    Article  Google Scholar 

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and also supported by Instituto Federal de Educação, Ciência e Tecnologia de São Paulo.

Author information

Authors and Affiliations

  1. Federal Institute of São Paulo and University of São Paulo, São Carlos, SP, Brazil

    Tamires Tessarolli de Souza Barbieri

  2. University of São Paulo, São Carlos, SP, Brazil

    Rudinei Goularte

Authors
  1. Tamires Tessarolli de Souza Barbieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rudinei Goularte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tamires Tessarolli de Souza Barbieri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barbieri, T.T.d.S., Goularte, R. Content selection criteria for news multi-video summarization based on human strategies. Int J Digit Libr 22, 1–14 (2021). https://doi.org/10.1007/s00799-020-00281-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00799-020-00281-9

Keywords

Search

Navigation