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A Method of Film Clips Retrieval Using Image Queries Based on User Interests

  • Ling ZouEmail author
  • Han Wang
  • Pei Chen
  • Bo Wei
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

The emergence of entertainment industry motivates the explosive growth of automatically film trailer. Manually finding desired clips from these large amounts of films is time-consuming and tedious, which makes finding the moments of user major or special preference becomes an urgent problem. Moreover, the user subjectivity over a film makes no fixed trailer meets all user interests. This paper addresses these problems by posing a query-related film clip extraction framework which optimizes selected frames to both semantically query-related and visually representative of the entire film. The experimental results show that our query-related film clip retrieval method is particularly useful for film editing, e.g. showing the abstraction of the entire film while playing focus on the parts that matches the user queries.

Keywords

Film trailer Multimedia retrieval Deep learning 

References

  1. 1.
    Gygli, M., Grabner, H., Riemenschneider, H., Gool, L.V.: Creating summaries from user videos. In: European Conference on Computer Vision, pp. 505–520 (2014)Google Scholar
  2. 2.
    Joshi, N., Kienzle, W., Toelle, M., Uyttendaele, M., Cohen, M.F.: Real-time hyperlapse creation via optimal frame selection. ACM Trans. Graph. 34(4), 63 (2015)CrossRefGoogle Scholar
  3. 3.
    Ghosh, J., Yong, J.L., Grauman, K.: Discovering Important People and Objects for Egocentric Video Summarization, vol. 157, no. 10, pp. 1346–1353 (2012)Google Scholar
  4. 4.
    Lu, Z., Grauman, K.: Story-driven summarization for egocentric video. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2714–2721 (2013)Google Scholar
  5. 5.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimed. Comput. Commun. Appl. 3(1), 3 (2007)CrossRefGoogle Scholar
  6. 6.
    Dan, B.G., Curless, B., Salesin, D., Seitz, S.M.: Schematic Storyboarding for Video Visualization and Editing, pp. 862–871 (2006)Google Scholar
  7. 7.
    Bacco, R., Lambert, P., Lambert, P., Ionescu, B.E.: Video summarization from spatio-temporal features. In: ACM Trecvid Video Summarization Workshop, pp. 144–148 (2008)Google Scholar
  8. 8.
    Liu, T., Kender, J.R.: Optimization algorithms for the selection of key frame sequences of variable length. In: European Conference on Computer Vision, pp. 403–417 (2002)Google Scholar
  9. 9.
    Potapov, D., Douze, M., Harchaoui, Z., Schmid, C.: Category-specific video summarization. In: European Conference on Computer Vision, pp. 540–555 (2014)Google Scholar
  10. 10.
    Yong, J.L., Ghosh, J., Grauman, K.: Discovering important people and objects for egocentric video summarization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1346–1353 (2012)Google Scholar
  11. 11.
    Gygli, M., Song, Y., Cao, L.: Video2gif: automatic generation of animated gifs from video. In: Computer Vision and Pattern Recognition, pp. 1001–1009 (2016)Google Scholar
  12. 12.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23, 368–375 (2018)CrossRefGoogle Scholar
  13. 13.
    Yao, T., Mei, T., Rui, Y.: Highlight detection with pairwise deep ranking for first-person video summarization. In: Computer Vision and Pattern Recognition, pp. 982–990 (2016)Google Scholar
  14. 14.
    Sun, M., Zeng, K.H., Lin, Y., Ali, F.: Semantic highlight retrieval and term prediction. IEEE Trans. Image Process. 26(7), 3303–3316 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mahasseni, B., Lam, M., Todorovic, S.: Unsupervised video summarization with adversarial LSTM networks. In: Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  16. 16.
    Li, Y., Lu, H., Li, J., Li, X., Li, Y., Serikawa, S.: Underwater image de-scattering and classification by deep neural network. Comput. Electr. Eng. 54, 68–77 (2016)CrossRefGoogle Scholar
  17. 17.
    Vasudevan, A.B., Gygli, M., Volokitin, A., Van Gool, L.: Query-Adaptive Video Summarization via Quality-Aware Relevance Estimation, pp. 582–590 (2017)Google Scholar
  18. 18.
    Kulesza, A., Taskar, B.: Determinantal point processes for machine learning. Found. Trends Mach. Learn. 5(2–3), 17 (2012)zbMATHGoogle Scholar
  19. 19.
    Zhang, K., Chao, W.L., Sha, F., Grauman, K.: Video summarization with long short-term memory. In: ECCV, pp. 766–782 (2016)Google Scholar
  20. 20.
    Azadi, S., Feng, J., Darrell, T.: Learning Detection with Diverse Proposals (2017)Google Scholar
  21. 21.
    Gong, B., Chao, W.L., Grauman, K., Sha, F.: Diverse sequential subset selection for supervised video summarization. In: International Conference on Neural Information Processing Systems, pp. 2069–2077 (2014)Google Scholar
  22. 22.
    Sharghi, A., Gong, B., Shah, M.: Query-focused extractive video summarization. In: European Conference on Computer Vision, pp. 3–19 (2016)Google Scholar
  23. 23.
    Sharghi, A., Laurel, J.S., Gong, B.: Query-Focused Video Summarization: Dataset, Evaluation, and a Memory Network Based Approach (2017)Google Scholar
  24. 24.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision, pp. 2818–2826 (2015)Google Scholar
  25. 25.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255 (2009)Google Scholar
  26. 26.
    Zhang, C.L., Luo, J.H., Wei, X.S., Wu, J.: In defense of fully connected layers in visual representation transfer. In: Pacific-Rim Conference on Multimedia (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Digital Media SchoolBeijing Film AcademyBeijingChina
  2. 2.School of Information and TechnologyBeijing Forestry UniversityBeijingChina
  3. 3.Hangzhou Dianzi UniversityZhejiangChina

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