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Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing

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3 Author(s)
Jianping Fan ; Dept. of Comput. Sci., Univ. of North Carolina, Charlotte, NC, USA ; Hangzai Luo ; Elmagarmid, A.K.

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.

Published in:

Image Processing, IEEE Transactions on  (Volume:13 ,  Issue: 7 )