Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Video Summarization Method for Movie Trailer-Genre Classification Based on Emotion Analysis

  • Conference paper
  • First Online:
Computing and Informatics (ICOCI 2023)

Abstract

We live in an information world where visual data undergo exponential growth within a very short time window. With diverging content diversity, we simply have no capacity to keep track of those data. While short video platforms (such as TikTok™ or YouTube Shorts™) can helped users viewing relevant videos within the shortest time possible, those videos might have misleading information, primarily if it is derived from long videos. Here, we analyzed several short videos (in terms of movie trailers) from YouTube and established a correlation between one movie trailer and the classified movie genre based on the emotion found in the trailer. This paper contributes to (1) an efficient framework to process the movie trailer and (2) a correlation analysis between the movie trailer and movie genre. We found that every movie genre can be represented by two unique emotions.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://bit.ly/3VTwcWS.

  2. 2.

    https://pypi.org/project/facial-emotion-recognition/.

References

  1. Cuesta-Valiño, P., Gutiérrez-Rodríguez, P., Durán-Álamo, P.: Why do people return to video platforms? millennials and centennials on TikTok. Media Commun. 10(1), 198–207 (2022)

    Article  Google Scholar 

  2. Gothankar, R., Troia, F.D., Stamp, M.: In: Stamp, M., Aaron Visaggio, C., Mercaldo, F., Di Troia, F. (eds.) Clickbait Detection for YouTube Videos, pp. 261–284. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97087-1_11

  3. Korsgaard, M.B.: Fake trailers as imaginary paratexts. MedieKultur: J. Media  Commun. Res. 36(68), 107–125 (2020)

    Google Scholar 

  4. Garcia, R., Watson, W.: Fake it while you make it: When do fantasy and science fiction movie trailers become deceptive advertising? In a Stranger Field. Studies of Art, Audiovisuals and New Technologies in Fantasy, SciFi and Horror Genres., 122

    Google Scholar 

  5. En, N.W., Mohd Pozi, M.S., Jatowt, A.: A face recognition module for video content analysis in malaysian parliament sessions. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, JCDL 2020, pp. 533–534. Association for Computing Machinery, New York  (2020).https://doi.org/10.1145/3383583.3398628

  6. DeMenthon, D., Kobla, V., Doermann, D.: Video summarization by curve simplification. In: Proceedings of the Sixth ACM International Conference on Multimedia, pp. 211–218 (1998)

    Google Scholar 

  7. Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content-based video retrieval and browsing. Pattern Recogn. 30(4), 643–658 (1997)

    Article  Google Scholar 

  8. Gong, Y., Liu, X.: Video summarization using singular value decomposition. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (Cat. No. PR00662), vol. 2, pp. 174–180. IEEE (2000)

    Google Scholar 

  9. Yahiaoui, I., Merialdo, B., Huet, B.: Automatic video summarization. In: Proceeding of CBMIR Conference (2001)

    Google Scholar 

  10. Farin, D., Effelsberg, W., de With, P.H.: Robust clustering-based video-summarization with integration of domain-knowledge. In: Proceedings of IEEE International Conference on Multimedia and Expo, vol. 1, pp. 89–92. IEEE (2002)

    Google Scholar 

  11. Corchs, S., Ciocca, G., Schettini, R.: Video summarization using a neurodynamical model of visual attention. In: IEEE 6th Workshop on Multimedia Signal Processing 2004, pp. 71–74. IEEE (2004)

    Google Scholar 

  12. Ngo, C.-W., Ma, Y.-F., Zhang, H.-J.: Video summarization and scene detection by graph modeling. IEEE Trans. Circuits Syst. Video Technol. 15(2), 296–305 (2005)

    Article  Google Scholar 

  13. Peng, Y., Ngo, C.-W.: Clip-based similarity measure for query-dependent clip retrieval and video summarization. IEEE Trans. Circuits Syst. Video Technol. 16(5), 612–627 (2006)

    Article  Google Scholar 

  14. Besiris, D., Makedonas, A., Economou, G., Fotopoulos, S.: Combining graph connectivity & dominant set clustering for video summarization. Multimedia Tools Appli. 44(2), 161–186 (2009)

    Article  Google Scholar 

  15. 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 

  16. Demir, M., Isil Bozma, H.: Video summarization via segments summary graphs. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 19–25 (2015)

    Google Scholar 

  17. Song, Y., Vallmitjana, J., Stent, A., Jaimes, A.: Tvsum: Summarizing web videos using titles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5179–5187 (2015)

    Google Scholar 

  18. Sharghi, A., Gong, B., Shah, M.: Query-focused extractive video summarization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016, Part VIII, pp. 3–19. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_1

  19. Al Nahian, M., Iftekhar, A., Islam, M.T., Rahman, S.M., Hatzinakos, D.: Cnn-based prediction of frame-level shot importance for video summarization. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS), pp. 24–29. IEEE (2017)

    Google Scholar 

  20. Zhou, K., Qiao, Y., Xiang, T.: Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  21. Lebron Casas, L., Koblents, E.: Video summarization with lstm and deep attention models. In: International Conference on MultiMedia Modeling, pp. 67–79. Springer (2019). 

    Google Scholar 

  22. Zhu, W., Lu, J., Li, J., Zhou, J.: Dsnet: a flexible detect-to-summarize network for video summarization. IEEE Trans. Image Process. 30, 948–962 (2021).https://doi.org/10.1109/TIP.2020.3039886

  23. Yoon, U.-N., Hong, M.-D., Jo, G.-S.: Interp-sum: Unsupervised video summarization with piecewise linear interpolation. Sensors 21(13), 4562 (2021)

    Article  Google Scholar 

  24. Goodfellow, I.J., et al.: Challenges in Representation Learning: A Report on Three Machine Learning Contests. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) Neural Information Processing, pp. 117–124. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42051-1_16

  25. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  26. Saini, P., Kumar, K., Kashid, S., et al.: Video summarization using deep learning techniques: a detailed analysis and investigation. Artif. Intell. Rev. (2023). https://doi.org/10.1007/s10462-023-10444-0

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the Ministry of Higher Education Malaysia for funding this study under the Fundamental Research Grant Scheme (Ref: FRGS/1/2019/ICT02/UUM/02/2, UUM S/O Code: 14358), and Research and Innovation Management Centre, Universiti Utara Malaysia for the administration of this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Syafiq Mohd Pozi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ng, W.E., Pozi, M.S.M., Omar, M.H., Katuk, N., Raziff, A.R.A. (2024). A Video Summarization Method for Movie Trailer-Genre Classification Based on Emotion Analysis. In: Zakaria, N.H., Mansor, N.S., Husni, H., Mohammed, F. (eds) Computing and Informatics. ICOCI 2023. Communications in Computer and Information Science, vol 2001. Springer, Singapore. https://doi.org/10.1007/978-981-99-9589-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9589-9_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9588-2

  • Online ISBN: 978-981-99-9589-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics