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.
Original language | English |
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Pages (from-to) | 99-113 |
Number of pages | 15 |
Journal | Multimedia Systems |
Volume | 22 |
Issue number | 1 |
Early online date | 13 Aug 2014 |
DOIs | |
Publication status | Published - Feb 2016 |
Externally published | Yes |