Abstract
The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance.
| Original language | English (US) |
|---|---|
| Title of host publication | ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 0769525717, 9780769525716 |
| DOIs | |
| State | Published - 2006 |
| Event | 22nd International Conference on Data Engineering Workshops, ICDEW 2006 - Atlanta, United States Duration: Apr 3 2006 → Apr 7 2006 |
Other
| Other | 22nd International Conference on Data Engineering Workshops, ICDEW 2006 |
|---|---|
| Country | United States |
| City | Atlanta |
| Period | 4/3/06 → 4/7/06 |
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ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
Cite this
Video Database Modeling and Temporal Pattern Retrieval using Hierarchical Markov Model Mediator. / Zhao, Na; Chen, Shu Ching; Shyu, Mei-Ling.
ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops. Institute of Electrical and Electronics Engineers Inc., 2006. 1623805.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Video Database Modeling and Temporal Pattern Retrieval using Hierarchical Markov Model Mediator
AU - Zhao, Na
AU - Chen, Shu Ching
AU - Shyu, Mei-Ling
PY - 2006
Y1 - 2006
N2 - The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance.
AB - The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance.
UR - http://www.scopus.com/inward/record.url?scp=84879669557&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879669557&partnerID=8YFLogxK
U2 - 10.1109/ICDEW.2006.162
DO - 10.1109/ICDEW.2006.162
M3 - Conference contribution
AN - SCOPUS:84879669557
BT - ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
ER -