Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing.
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.
Fan
Jianping
J
Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA. jfan@uncc.edu
Luo
Hangzai
H
Elmagarmid
Ahmed K
AK
D000043:Abstracting and Indexing / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D004282:Documentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D012660:Semantics; D014584:User-Computer Interface; D014741:Video Recording / Q000145:classification* / Q000379:methods