镜头切换检测是视频结构化的第一步。
Shot boundary detection is the first step in video structure.
视频分割是视频结构化和检索的重要技术，目前主要通过镜头分割得到。
Video segmentation is an important technology for video structuring and retrieval, with most research work now focused on shot detection.
为了进行视频结构化和视频内容分析，需要准确有效地提取视频镜头的边界信息。
To analyze the video structure and content, shot boundary features should be extracted exactly and efficiently.
视频结构化的目的是把视频分为镜头、场景和序列等不同层次的逻辑单元，以便于在不同结构层次上对视频进行检索和浏览。
The purpose of the structure of video is to turn video into different hierarchical logic clips of shot, scene and series.
本文主要针对基于内容的视频信息分类检索中的若干问题展开讨论研究，包括视频结构化、特征提取、特征优化以及特征分类等等。
In this paper, problems of the video information classification and retrieval are discussed, including video structuring, feature abstracting, feature optimizing and feature classifying, etc.
UIMA是一个用于分析非结构化内容(比如文本、视频和音频)的组件架构和软件框架实现。
UIMA is a component architecture and a software framework implementation for the analysis of unstructured content such as text, video, and audio.
我所谓的强大是指，那些解决方案必须能够从结构化数据(例如数据库和网页)和非结构化数据(例如文本、音频和视频)中提取可操作的信息。
By strong, I mean they must be able to extract actionable information from both structured data, such as databases and Web pages, and unstructured data, such as text, audio, and video.
“许多人没有真正意识到非结构化数据(比如视频、音频和图像)蕴含着重要信息，但它们确实包含重要信息，”Deutsch说。
"A lot of people don't really think of unstructured data-such as video, audio, and images-as holding important information, but it does," Deutsch says.
由于视频、音频、图形和web应用程序生成天文数量的数据，非结构化数据的数量正在而且会继续呈指数增加。
The amount of unstructured data is and will continue to increase exponentially due to astronomical data generated from videos, audios, graphics and web applications.
网民们为非结构化的信息比如照片和视频打上标签方便查找。
Internet users help to label unstructured information so it can be easily found, tagging photos and videos.
公司还可能需要分析半结构化文本(比如XML内容)或其他数据类型(比如音频和视频)。
Companies may also have a need to analyze semi-structured text (such as XML content) or other data types (such as audio and video).
使用这些数据模型，可以捕获所有类型的内容(例如音频、视频和文本)中的结构化信息和关系信息，并对结构化内容与非结构化内容进行集成。
With it you can capture structural and relationship information across all types of content (such as audio, video, and text) and integrate structured data with unstructured content.
相反，非结构化信息包括了免费的文本报告、文档、web页面、生命科学数据、音频、视频等等。
Unstructured information, in contrast, involves free text reports, documents, Web pages, life science data, audio, video, and so on.
最后，应用一个结构化视频内容的编目原型系统，证明了该技术的有效性。
At last, a video cataloging prototype system of structured video content is used to prove the effectiveness of the technology.
提出适合视频对象合成的结构化视频模型，分析视频对象的结构特点。
This paper presents a video model suited for video object composition and analyzes the structured feature of video object.
最后，应用一个结构化视频内容的编目原型系统，证明了该技术的有效性。
At last, a video cataloging prototype system of structured video content is used to prove...
然后基于语义人脸实现了主持人镜头检测和视频新闻结构化算法，体现了视频对象正视频内容分析中的基础作用。
Then we realize anchorperson shot detection and video news indexing based on semantic faces, which shows video object's important usage in analysis of video semantic contents.
实验表明，该算法在视频新闻结构化中可以得到较好的应用。
Structure used to index and explorer the video news is established. Experiment shows this algorithm works well for video news indexing.
对视频内容的结构化及视频数据内容模型进行了详细分析，着重讨论了视频信息的结构化、视频内容的元数据描述、描述生成等方面的关键性技术问题。
Detailed analyze structured video content and model of video content, discuss the key problem of presentation of video information, metadata description of content and the produce of description etc.
对视频内容的结构化及视频数据内容模型进行了详细分析，着重讨论了视频信息的结构化、视频内容的元数据描述、描述生成等方面的关键性技术问题。
Detailed analyze structured video content and model of video content, discuss the key problem of presentation of video information, metadata description of content and the produce of description etc.
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