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Online web video topic detection and tracking with semi-supervised learning

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Abstract

With the pervasiveness of online social media and rapid growth of web data, a large amount of multi-media data is 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 adaptable 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.

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Acknowledgments

This work was supported by China Postdoctoral Science Foundation: 2012M520436, in part by National Basic Research Program of China (973 Program): 2012CB316400, National Natural Science Foundation of China: 61303153, 61025011, 61332016, 61322212, 61202234 and 61202322, Present Foundation of UCAS.

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Authors and Affiliations

  1. School of Computer and Control Engineering, University of the Chinese Academy of Sciences, Beijing, China

    Guorong Li & Qingming Huang

  2. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China

    Shuqiang Jiang & Qingming Huang

  3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Weigang Zhang

  4. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing, China

    Junbiao Pang

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  1. Guorong Li
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Correspondence to Guorong Li, Weigang Zhang or Qingming Huang.

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Li, G., Jiang, S., Zhang, W. et al. Online web video topic detection and tracking with semi-supervised learning. Multimedia Systems 22, 115–125 (2016). https://doi.org/10.1007/s00530-014-0402-0

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