A Research on Extraction and Application of Video Keyword based-on Large-Scale Video Archive

基于大规模视频档案的视频关键词提取及应用研究

基本信息

批准号：
15500082
负责人：
SATOH Shinichi
金额：
$ 2.3万
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：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

We proposed a technique to extract "video keywords" which are characteristic regions in videos, and we studied its applications. Video keywords can be identified each other in videos, and thus they enable video indexing based on characteristic objects, buildings, people, particular regions in background, and iconic regions, which ordinary approaches for typical image retrieval using indexing by global color distribution cannot handle.Video keyword extraction requires image segmentation to locate object region in general, however, it is said that the perfect segmentation is impossible without semantics information of the contents of the image, which cannot be assumed. Therefore video keyword extraction can be hard task. On the other hand, keyword extraction for text analysis is robustly realized based on statistical information of the occurrence of each keyword in large-scale text corpus, such as TFIDF, to select important words, i.e., keyword extraction does not rely on semantics information. Basic idea of the presented research is to apply similar technique to video domain, i.e., extract frequently used video segments as video keywords assuming that they correspond to important visual information.It obviously requires face image matching to realize video keyword extraction from large-scale video archives. So we proposed Generalized Histogram as an optimized feature representation based on luminosity distribution histogram, and its effectiveness was demonstrated. Moreover, the image browser using identical video segment detection to extract video keywords was developed to show its practical effectiveness of video keywords. In addition, the quasi high-dimensional clustering method was developed to further speed up the image matching.

我们提出了一种提取视频中特征区域“视频关键词”的技术，并研究了其应用。视频关键字可以在视频中相互识别，因此可以基于特征对象、建筑物、人物、背景中的特定区域和标志性区域进行视频索引，这是使用全局颜色分布索引的典型图像检索的普通方法无法处理的。视频关键词提取一般需要图像分割来定位目标区域，但是，如果没有图像内容的语义信息，完美的分割是不可能的，这是不能假设的。因此，视频关键词提取可能是一项艰巨的任务。另一方面，文本分析的关键词提取是基于大规模文本语料库（如TFIDF）中每个关键词出现的统计信息，鲁棒地实现的，以选择重要的单词，即关键词提取不依赖于语义信息。本研究的基本思想是将类似的技术应用于视频领域，即假设它们对应于重要的视觉信息，则提取常用的视频片段作为视频关键词。显然需要人脸图像匹配来实现大规模视频中的视频关键词提取档案。因此我们提出了广义直方图作为基于光度分布直方图的优化特征表示，并证明了其有效性。此外，还开发了使用相同视频片段检测来提取视频关键词的图像浏览器，以展示其视频关键词的实际有效性。此外，还开发了准高维聚类方法以进一步加快图像匹配速度。