Abstract
Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.
| Original language | English |
|---|---|
| Pages (from-to) | 974-992 |
| Number of pages | 19 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 13 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Jul 2004 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Graphics and Computer-Aided Design
- Software
- Theoretical Computer Science
- Computational Theory and Mathematics
- Computer Vision and Pattern Recognition
Cite this
Concept-oriented indexing of video databases : Toward semantic sensitive retrieval and browsing. / Fan, Jianping; Luo, Hangzai; Elmagarmid, Ahmed.
In: IEEE Transactions on Image Processing, Vol. 13, No. 7, 01.07.2004, p. 974-992.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Concept-oriented indexing of video databases
T2 - Toward semantic sensitive retrieval and browsing
AU - Fan, Jianping
AU - Luo, Hangzai
AU - Elmagarmid, Ahmed
PY - 2004/7/1
Y1 - 2004/7/1
N2 - Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.
AB - Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.
UR - http://www.scopus.com/inward/record.url?scp=3142671430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=3142671430&partnerID=8YFLogxK
U2 - 10.1109/TIP.2004.827232
DO - 10.1109/TIP.2004.827232
M3 - Article
C2 - 15648863
AN - SCOPUS:3142671430
VL - 13
SP - 974
EP - 992
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 7
ER -