Multimedia Database Technologies for Sophisticated Analysis of Large-Scale Video Corpora

用于大规模视频语料库复杂分析的多媒体数据库技术

• 批准号: 18500094
• 负责人: KATAYAMA Norio
• 金额: $ 2.52万
• 依托单位: National Institute of Informatics
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500094/
• 关键词: video corpus; database; contents; archive; algorithm; information system

The cost of building a huge multimedia archive has been significantly reduced. Especially, broadcast video archives are useful for multimedia research since they involve a wide variety of contents. It is naturally perceived that a huge amount of broadcast video would be a useful corpus for multimedia indexing and mining research. Text corpora are widely used for text processing research. They contribute to the advancement of text processing techniques. The same phenomenon may arise for multimedia information processing. However, in general, video analysis is exhaustively time consuming. It is quite common to take one hour for one PC to analyze one-hour video. This means that we need hundreds of PCs to process thousands hours of video in a day. In near future, it may be common to process thousands of videos with hundreds of CPU cores. Thus, HPC and DB Technologies would also be an important element for large-scale multimedia information processing. From this viewpoint, we conducted a preliminary study on multimedia database technologies for analyzing large-scale video corpora. We extracted about 550, 000 camera shots from our video archive which records a daily news program for seven years (2, 400 days/1, 200 hours). By choosing a representative frame from each camera shot, we obtained a collection containing wide variety of images. Then, we applied our indexing techniques, including SR-Tree (a multidimensional index structure) and DSNN (distinctiveness-sensitive nearest-neighbor search algorithm), to examine the effectiveness of database technologies. According to our performance experiments, we confirmed that database technologies are quite effective for reducing both computation and I/O costs of multimedia indexing.

建立庞大多媒体档案的成本已显着降低。特别是，广播视频档案对于多媒体研究非常有用，因为它们涉及广泛的内容。人们自然认为大量的广播视频将成为多媒体索引和挖掘研究的有用语料库。文本语料库广泛用于文本处理研究。它们为文本处理技术的进步做出了贡献。对于多媒体信息处理也可能出现同样的现象。然而，一般来说，视频分析非常耗时。一台电脑分析一小时的视频需要一小时的情况很常见。这意味着我们每天需要数百台电脑来处理数千小时的视频。在不久的将来，使用数百个 CPU 核心处理数千个视频可能会很常见。因此，HPC和DB技术也将成为大规模多媒体信息处理的重要元素。从这个角度出发，我们对用于分析大规模视频语料的多媒体数据库技术进行了初步研究。我们从视频档案中提取了大约 550, 000 个摄像机镜头，该档案记录了七年的每日新闻节目（2, 400 天/1, 200 小时）。通过从每个相机镜头中选择具有代表性的帧，我们获得了包含各种图像的集合。然后，我们应用我们的索引技术，包括SR-Tree（一种多维索引结构）和DSNN（独特性敏感的最近邻搜索算法）来检查数据库技术的有效性。根据我们的性能实验，我们证实数据库技术对于降低多媒体索引的计算和 I/O 成本非常有效。