Abstract
With the rapid growth of web data, a large amount of web videos are available online. However, how to organize them for facilitating users’ experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adapt to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking.
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
Preview
Unable to display preview. Download preview PDF.
References
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report (1998)
Chen, K., Luesukprasert, L., Chou, S.: Hot topic extraction based on timeline analysis and multi-dimensional sentence modeling. IEEE Transactions on Knowledge Data Engeering 19(8), 1016–1025 (2007)
Sun, A.X., Hu, M.: Query-guided event detection from news and blog streams. IEEE Transactions on Systems, Man and Cybernetics 41(5), 834–839 (2011)
Zhai, Y., Shah, M.: Tracking news stories across different sources. In: Proceedings of the 20th ACM International Conference on Multimedia, MM 2005, pp. 2–10. ACM (2005)
Kasiviswanathan, S.P., Melville, P., Banerjee, A., Sindhwani, V.: Emerging topic detection using dictionary learning. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 745–754. ACM (2011)
Bao, B.K., Min, W., Sang, J., Xu, C.: Multimedia news digger on emerging topics from social streams. In: Proceedings of the 20th ACM International Conference on Multimedia, MM 2012, pp. 1357–1358. ACM, New York (2012)
Shao, J., Ma, S., Lu, W., Zhuang, Y.: A unified framework for web video topic discovery and visualization. Pattern Recognition Letters 33(4), 410–419 (2012)
Hong, R., Tang, J., Tan, H., Ngo, C., Yan, S., Chua, T.: Beyond search: event driven summarization for web videos. ACM Transactions on Multimedia Computing, Communications and Applications 33(4), 410–419 (2011)
Cao, J., Ngo, C.W., Zhang, Y.D., Li, J.T.: Tracking web video topics: Discovery, visualization, and monitoring. IEEE Transactions on Circuits and Systems for Video Technology 21(12), 1835–1846 (2011)
Chen, T., Liu, C., Huang, Q.: An effective multi-clue fusion approach for web video topic detection. In: Proceedings of the 20th ACM International Conference on Multimedia, MM 2012, pp. 781–784. ACM, New York (2012)
Yang, Y., Song, J., Huang, Z., Ma, Z., Sebe, N., Hauptmann, A.: Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Transactions on Multimedia 15(3), 572–581 (2013)
Freund, Y., Schapire, R.: A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Hastie, T., Simard, P.: Models and metrics for handwritten character recognition. Statistical Science 13(1), 54–65 (1998)
Cao, J., Zhang, Y., Song, Y., Chen, Z., Zhang, X., Li, J.: Mcg-webv: A benchmark dataset for web video analysis (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, G., Zhang, W., Pang, J., Huang, Q., Jiang, S. (2013). Online Web-Video Topic Detection and Tracking with Semi-supervised Learning. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-03731-8_70
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
eBook Packages: Computer ScienceComputer Science (R0)