Abstract
With the boom of online video uploading, video tagging becomes an important way for video indexing. However, text-based video tagging methods ignore either genre labels or temporal differences of videos, which makes results defective. Fortunately, a new type of videos called time-sync commented videos which contains large amounts of information commented by the users helps videos tagging. In this paper, we propose a supervised dynamic Latent Dirichlet Allocation model utilizing the variational topics of time-sync comments to extract both genre labels and keywords as tags. We also implement experiments on large scale real-world datasets and the effectiveness of our model are proved both in genre label classification and keyword extraction compared with baseline models.
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These words and topic names are manually translated to English by the authors.
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A Chinese internet slang means laughing.
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This work is partially supported by National Key Research and Development Program of China.
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Zeng, Z., Xue, C., Gao, N., Wang, L., Liu, Z. (2018). Learning from Audience Intelligence: Dynamic Labeled LDA Model for Time-Sync Commented Video Tagging. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_48
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DOI: https://doi.org/10.1007/978-3-030-04182-3_48
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