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
N1 - Funding Information:
For Shu-Ching Chen, this research was supported in part by NSF EIA-0220562 and HRD-0317692. For Mei- Ling Shyu, this research was supported in part by NSF ITR (Medium) IIS-0325260
Publisher Copyright:
© 2006 IEEE.
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
T3 - ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops
BT - ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops
A2 - Zhou, Xiaofang
A2 - Barga, Roger S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Data Engineering Workshops, ICDEW 2006
Y2 - 3 April 2006 through 7 April 2006
ER -