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Social video annotation by combining features with a tri-adaptation approach

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Abstract

Online social video websites such as YouTube allow users to manually annotate their video documents with textual labels. These labels can be used as indexing keywords to facilitate search and organization of video data. However, manual video annotation is usually a labor-intensive and time-consuming process. In this work, we propose a novel social video annotation approach that combines multiple feature sets based on a tri-adaptation approach. For the shots in each video, they are annotated by aggregating models that are learned from three complementary feature sets. Meanwhile, the models are collaboratively adapted by exploring unlabeled shots. In this sense, the method can be viewed as a novel semi-supervised algorithm that explores three complementary views. Our approach also exploits the temporal smoothness of video labels by applying a label correction strategy. Experiments on a web video dataset demonstrate the effectiveness of the proposed approach.

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Acknowledgments

The authors sincerely appreciate the useful comments and suggestions from the anonymous reviewers. This work was supported by National Natural Science Fund of China (Grant No. 61272214, 61173104, 61301222), China Postdoctoral Science Foundation (Grant No. 2013M541821), Fundamental Research Funds for the Central Universities (Grant No. 2013HGQC0018, 2013HGBH0027, 2013HGBZ0166).

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Authors and Affiliations

  1. Liaoning University of Technology, Jinzhou, 121001, People’s Republic of China

    Fuming Sun & Meixiang Xu

  2. Dalian University of Technology, Dalian, 115024, People’s Republic of China

    Haojie Li

  3. School of Computer and Information, and Computer Science and Technology Postdoctoral Research Station, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Shijie Hao

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  1. Fuming Sun
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Correspondence to Shijie Hao.

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Sun, F., Xu, M., Li, H. et al. Social video annotation by combining features with a tri-adaptation approach. Multimedia Systems 22, 413–422 (2016). https://doi.org/10.1007/s00530-014-0405-x

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