Accurate online video tagging via probabilistic hybrid modeling |
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Authors: | Jialie Shen Meng Wang Tat-Seng Chua |
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Affiliation: | 1.School of Information Systems,Singapore Management University,Singapore,Singapore;2.Hefei University of Technology,Hefei,China;3.Department of Computer Science,National University of Singapore,Singapore,Singapore |
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Abstract: | Accurate video tagging has been becoming increasingly crucial for online video management and search. This article documents a novel framework called comprehensive video tagger (CVTagger) to facilitate accurate tag-based video annotation. The system applies both multimodal and temporal properties combined with a novel classification framework with hierarchical structure based on multilayer concept model and regression analysis. The advanced architecture enables effective incorporation of both video concept dependency and temporal dynamics. Using a large-scale test collection containing 50,000 YouTube videos, a set of empirical studies have been carried out and experimental results demonstrate various advantages of CVTagger over the state-of-the-art techniques. |
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