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Medical video mining for efficient database indexing, management and access

机译：医学视频挖掘可实现高效的数据库索引，管理和访问

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摘要

To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
机译：为了实现更有效的视频索引和访问，我们引入了视频数据库管理框架以及用于视频内容结构和事件挖掘的策略。视频镜头分割和代表性帧选择策略首先用于将连续视频流解析为物理单元。然后提出了视频镜头分组，组合并和场景聚类方案，以便使用聚类的场景，场景，组和镜头将视频镜头组织为分层结构，并且从上到下逐渐增加粒度。然后，集成音频和视频处理技术以从检测到的场景中挖掘事件信息，例如对话，演示和临床操作。最后，将获取的视频内容结构和事件集成在一起，以构建可伸缩的视频浏览工具，该工具可用于可视化视频内容层次结构和事件信息，以进行有效访问。实验结果也被提出来评估所提出的框架和算法的性能。

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