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Localizing relevant frames in web videos using topic model and relevance filtering

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Abstract

Numerous web videos associated with rich metadata are available on the Internet today. While such metadata like video tags bring us facilitation and opportunities for video search and multimedia content understanding, some challenges also arise due to the fact that those video tags are usually annotated at the video level, while many tags actually only describe parts of the video content. How to localize the relevant parts or frames of web video for given tags is the key to many applications and research tasks. In this paper we propose combining topic model and relevance filtering to localize relevant frames. Our method is designed in three steps. First, we apply relevance filtering to assign relevance scores to video frames and a raw relevant frame set is obtained by selecting the top ranked frames. Then, we separate the frames into topics by mining the underlying semantics using latent Dirichlet allocation and use the raw relevance set as validation set to select relevant topics. Finally, the topical relevances are used to refine the raw relevant frame set and the final results are obtained. Experiment results on two real web video databases validate the effectiveness of the proposed approach.

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Acknowledgments

This work was supported by National Natural Science Funds of China (61033012, 61173104,61202133).

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, China

    Haojie Li, Lei Yi, Bin Liu & Yi Wang

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  1. Haojie Li
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  2. Lei Yi
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  3. Bin Liu
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  4. Yi Wang
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Correspondence to Haojie Li.

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Li, H., Yi, L., Liu, B. et al. Localizing relevant frames in web videos using topic model and relevance filtering. Machine Vision and Applications 25, 1661–1670 (2014). https://doi.org/10.1007/s00138-013-0537-6

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  • DOI: https://doi.org/10.1007/s00138-013-0537-6

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