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.
Online video, Social multimedia, Tagging
Computer Sciences | Databases and Information Systems
Data Management and Analytics
SHEN, Jialie; WANG, Meng; and CHUA, Tat-Seng.
Accurate Online Video Tagging via Probabilistic Hybrid Modeling. (2016). Multimedia Systems. 22, (1), 99-113. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2457