1 October 2001 Multi-level video content represntation and retrieval
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In this article, several practical algorithms are proposed to support content-based video analysis, modeling, representation, summarization, indexing, and access. First, a multilevel video database model is given. One advantage of this model is that it provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic concepts from a human point of view. Second, several model-based video analysis techniques are proposed. In order to detect the video shots, we present a novel technique, which can adapt the threshold for scene cut detection to the activities of variant videos or even different video shots. A seeded region aggregation and temporal tracking technique is proposed for generating the semantic video objects. The semantic video scenes can then be generated from these extracted video access units (e.g., shots and objects) according to some domain knowledge. Third, in order to categorize video contents into a set of semantic clusters, an integrated video classification technique is developed to support more efficient multilevel video representation, summarization, indexing, and access techniques.
© (2001) Society of Photo-Optical Instrumentation Engineers (SPIE)
Jianping Fan, Jianping Fan, Walid G. Aref, Walid G. Aref, Ahmed K. Elmagarmid, Ahmed K. Elmagarmid, Mohand-Said Hacid, Mohand-Said Hacid, Mirette S. Marzouk, Mirette S. Marzouk, Xingquan Zhu, Xingquan Zhu, } "Multi-level video content represntation and retrieval," Journal of Electronic Imaging 10(4), (1 October 2001). https://doi.org/10.1117/1.1406944 . Submission:


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