Abstract
With more and more lecture, videos are available on the Internet, on-line learning and e-learning are getting increasing concerns because of many advantages such as high degree of interactivity. The semantic content discovery for lecture video is a key problem. In this paper, we propose a Multi-modal LDA model, which discovers the semantic topics of lecture videos by considering audio and visual information. Specifically, the speaking content and the information of presentation slides are extracted from the lecture videos. With the proposed inference and learning algorithm, the semantic topics of the video can be discovered. The experimental results show that the proposed method can effectively discover the meaningful semantic characters of the lecture videos.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hofmann, T.: Probabilistic latent semantic indexing. In: ACM SIGIR Forum, vol. 51, no. 2, pp. 211–218. ACM (2017)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228–5235 (2004)
Azaiez, I., Ben Ahmed, M.: An approach of a semantic annotation and thematisation of AV documents. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 1406–1411. IEEE (2010)
Lee, L.-S., Chen, S.-C., Ho, Y., Chen, J.-F., Li, M.-H., Li, T.-H.: An initial prototype system for Chinese spoken document understanding and organization for indexing/browsing and retrieval applications. In: 2004 International Symposium on Chinese Spoken Language Processing, pp. 329–332. IEEE (2004)
Li, W., Zhou, X., Chai, T.: “Bag of visual words” and latent semantic analysis-based burning state recognition for rotary kiln sintering process. In: Control and Decision Conference (CCDC), 2011 Chinese, pp. 377–382. IEEE (2011)
Ide, I., Mo, H., Katayama, N., Satoh, S.: Exploiting topic thread structures in a news video archive for the semi-automatic generation of video summaries. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 1473–1476. IEEE (2006)
Mase, M., Yamada, S., Nitta, K.: Extracting topic maps from Web pages by Web link structure and content. In: Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence). IEEE Congress on, pp. 1232–1239. IEEE (2008)
Huang, R., Lin, F., Shi, Z.: Focused crawling with heterogeneous semantic information. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 1, pp. 525–531. IEEE (2008)
Hennig, L., Strecker, T., Narr, S., De Luca, E.W., Albayrak, S.: Identifying sentence-level semantic content units with topic models. In: 2010 Workshop on Database and Expert Systems Applications (DEXA), pp. 59–63. IEEE (2010)
Zhang, W., Qin, Z., Wan, T.: Image scene categorization using multi-bag-of-features. In: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1804–1808. IEEE (2011)
Kong, S.-Y., Lee, L.-S.: Semantic analysis and organization of spoken documents based on parameters derived from latent topics. IEEE Trans. Audio Speech Lang. Process. 19(7), 1875–1889 (2011)
Liu, H., Liu, G., Lv, Y.: Semantic hyperlink analysis model. In: Fourth International Conference on Semantics, Knowledge and Grid, SKG 2008, pp. 416–419. IEEE (2008)
Vretos, N., Nikolaidis, N., Pitas, I.: A perceptual hashing algorithm using latent Dirichlet allocation. In: IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 362–365. IEEE (2009)
Ngo, C.-W., Pong, T.-C., Huang, T.S.: Detection of slide transition for topic indexing. In: 2002 IEEE International Conference on Multimedia and Expo, ICME 2002. Proceedings, vol. 2, pp. 533–536. IEEE (2002)
Mirylenka, K., Miksovic, C., Scotton, P.: Applicability of latent Dirichlet allocation for company modeling. In: Industrial Conference on Data Mining (ICDM-2016) (2016)
Hu, C., Cao, H., Gong, Q.: Sub-Gibbs sampling: a new strategy for inferring LDA. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 907–912. IEEE (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bian, J., Huang, M.L. (2020). Semantic Topic Discovery for Lecture Video. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-29516-5_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29515-8
Online ISBN: 978-3-030-29516-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)