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Multimedia Systems

, Volume 22, Issue 1, pp 99–113 | Cite as

Accurate online video tagging via probabilistic hybrid modeling

  • Jialie Shen
  • Meng Wang
  • Tat-Seng Chua
Special Issue Paper

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.

Keywords

Online video Social multimedia Tagging 

Notes

Acknowledgments

Jialie Shen is supported by Academic Research Fund (AcRF) Tier-2 (MOE2013-T2-2-156), Ministry of Education (MOE), Singapore.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Information SystemsSingapore Management UniversitySingaporeSingapore
  2. 2.Hefei University of TechnologyHefeiChina
  3. 3.Department of Computer ScienceNational University of SingaporeSingaporeSingapore

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