Research on Digital Archives for Human Motion Data and Video Data

在视频数据库的研究中，为了快速、准确地检索视频数据，需要对视频数据进行索引。索引视频数据的最重要过程是将视频分割成称为场景的连贯单元，而不仅仅是我们称为剪切的部分视频。手动分割是准确的，但它需要大量的时间和关于视频内容的背景知识。本文提出了一种基于颜色、文本和时间信息的视频自动分割方法，并对人体运动数据进行了标记。运动数据具有以下特征：某些身体部位上的运动影响其他身体部位上的运动。我们称这种依赖关系为运动关联规则。因此，标记运动数据的任务等同于使用运动关联规则来表示运动的任务。由于人体运动数据具有上述相关性，我们将运动数据作为多流处理。为了找到运动关联规则，减少任务的开销，我们将高维的运动数据转换成多个低维的符号序列。每个符号唯一地表示原始运动。符号序列可以通过基于内容的分段和相似段的聚类来获得。

In researches on video database, indexing video data is necessary for quick and correct retrieval. The most important process in indexing video data is to segment video into coherent units called scenes, more than just partial video that we call cuts. Manual segmentation is accurate, but it takes a lot of time and background knowledge about the content of video. We have developed the method to segment video automatically, by integrating color, textual, and temporal information extracted from video.Furthermore, we have focused on tagging human motion data. The motion data has the following features : movements on some body parts influence those on other body parts. We call this dependency as motion association rule. Thus the task of tagging motion data is equal to the task of expressing motion by using motion association rules. Because human motion data has above dependency, we treat motion data as multistream. In order to find motion association rules and cut off the cost of the task, we convert motion data of high dimension into multiple sequences of symbols of lower dimension. Each symbol uniquely represents primitive motion. The sequences of symbols can be obtained by content-based segmentation and clustering of similar segments.