Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2016
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
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Multimedia Systems
Volume
22
Issue
1
First Page
99
Last Page
113
ISSN
0942-4962
Identifier
10.1007/s00530-014-0399-4
Publisher
Springer
Citation
SHEN, Jialie; WANG, Meng; and CHUA, Tat-Seng.
Accurate Online Video Tagging via Probabilistic Hybrid Modeling. (2016). Multimedia Systems. 22, (1), 99-113.
Available at: https://ink.library.smu.edu.sg/sis_research/2457
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00530-014-0399-4