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 |
|---|---|
| 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 |
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Accurate online video tagging via probabilistic hybrid modeling. / Shen, Jialie; Wang, Meng; Chua, Tat Seng.
In: Multimedia Systems, Vol. 22, No. 1, 02.2016, p. 99-113.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Accurate online video tagging via probabilistic hybrid modeling
AU - Shen, Jialie
AU - Wang, Meng
AU - Chua, Tat Seng
PY - 2016/2
Y1 - 2016/2
N2 - 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.
AB - 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.
KW - Online video
KW - Social multimedia
KW - Tagging
UR - http://www.scopus.com/inward/record.url?scp=84955484628&partnerID=8YFLogxK
U2 - 10.1007/s00530-014-0399-4
DO - 10.1007/s00530-014-0399-4
M3 - Article
VL - 22
SP - 99
EP - 113
JO - Multimedia Systems
JF - Multimedia Systems
SN - 0942-4962
IS - 1
ER -