Our project on digital video modeling aims to achieve efficient browsing and retrieval. Video is not merely a huge collection of still images, it is a complex temporal medium capturing high-level semantic ideas. For almost a decade researchers have worked on developing efficient techniques to model, index, and retrieve digital video information. To support intuitive video access, the video data must be properly modeled and indexed based on its characteristics and contents. One such annotation-based model is the stratification model, which focuses on segmenting the video's contextual information into multiple layers or strata. Each stratum describes the temporal occurrences of a simple concept such as the appearance of an anchor person in a news video. It thus focuses on segmenting the contextual information into chunks rather than dividing physically contiguous frames into shots, as is traditionally done.
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