Title
An Integrated and Interactive Video Retrieval Framework with Hierarchical Learning Models and Semantic Clustering Strategy
Abstract
In this research, we propose an integrated and interactive framework to manage and retrieve large scale video archives. The video data are modeled by a hierarchical learning mechanism called HMMM (hierarchical Markov model mediator) and indexed by an innovative semantic video database clustering strategy. The cumulated user feedbacks are reused to update the affinity relationships of the video objects as well as their initial state probabilities. Correspondingly, both the high level semantics and user perceptions are employed in the video clustering strategy. The clustered video database is capable of providing appealing multimedia experience to the users because the modeled multimedia database system can learn the user's preferences and interests interactively
Year
DOI
Venue
2006
10.1109/IRI.2006.252454
IRI
Keywords
Field
DocType
hierarchical markov model mediator,interactive video retrieval,pattern clustering,hierarchical learning model,integrated video retrieval,semantic video database clustering,multimedia database system,video databases,markov processes,video retrieval,large scale video archive,cumulant,indexation
Semantic clustering,Multimedia database,Markov process,Information retrieval,Computer science,Interactive video retrieval,Learning models,Cluster analysis,Perception,Semantics
Conference
ISBN
Citations 
PageRank 
0-7803-9788-6
3
0.40
References 
Authors
4
4
Name
Order
Citations
PageRank
Na Zhao1948.85
Shu-ching Chen231.75
Mei-Ling Shyu31863141.25
Stuart Harvey Rubin47320.96