实验结果显示，该方法能够有效解决视频语义内容建模和分析匹配问题。
The results of the experiment demonstrate the validity of the presented method, which can solve the problems of video semantic modeling and matching.
视频语义内容的自动获取和分析是计算机视觉领域最根本的目的和最具有挑战性的任务。
The automatic retrieval and analysis of video semantic contents is the fundamental goal and most challenging task in computer vision.
最后，结合以上新方法，提出了一种基于MPEG - 7视频语义内容的层次化描述方案。
Finally, according to the new method, we propose a hierarchical video content description scheme under the international standard MPEG-7.
作为视频语义内容的最基本表现形式，视频对象的检测和识别在近年来一直是这一领域的研究热点。
And recently, detection and recognition of video objects is one of the most important research areas in video semantic contents analysis.
摄像机运动检测有助于实现视频语义内容的理解和分析，是基于内容的视频检索系统中的重要研究内容。
Camera motion detection contribute to understand and analyze video content, camera motion detection has been an important research content in video retrieval system.
情节代表帧选取方法是视频语义分析和基于内容的视频检索的很重要的方法。
Selecting episode representation frame is one of the important processes in video semantic analysis and content-based video retrieval.
由于输入视频序列的每一帧被分割成任意形状的视频对象平面(VOP)，这样每个VOP描述了一个语义意义的对象或所感兴趣的视频内容。
Each frame of the input sequence is segmented into arbitrarily shaped image regions (VOP's) such that each VOP describes one semantically meaningful object or video content of interest.
对于视频序列，则介绍了镜头检测、镜头内容表示、场景的语义描述等技术。
For video sequences, we introduce such techniques as shot detection, representation of shot content, semantic scene description.
本文从镜头分割和分类分析了视频内容的语义结构。
In this paper, we mainly discuss semantic structure of content based video by shot segmentation and classification.
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.
进一步说，目前基于内容的视频检索的语义处理理论和技术仍有大量问题有待深入研究。
That is to say, a lot of problems concerning semantic processing theory and technology of CBVR need a further study.
提出了基于基本语义单元的体育视频内容分析框架，研究了这一框架中的慢镜头分析。
The main jobs in this paper lie in:1. It has proposed a framework based on Basic Semantic Unit (BSU) for sports video content analysis and has studied slow-motion replay analysis in the framework.
本文主要针对语义层次上的视频内容结构化分析和表达进行了研究。
In this thesis, we mainly develop the methods for video content analysis as well as video content representation on a semantic level.
本文研究内容涉及特征索引方法、视频语义分类方法、相关反馈方法、视频片段的相似度量以及视觉内容特征的表示等问题。
The research contents include high dimension feature vectors index, semantic video classification, relevance feedback, similarity measure of video clips and video content representation.
视频语义信息是指描述视频中的物体形状、物体之间空间关系以及物体的事件等内容的信息。
Video semantic information is referred to the information about the objects in video streams, including object shape, spatial relation between objects, and events related to objects, etc.
视频文字作为一种高级语义信息，对视频内容的理解、索引具有重要作用。
As a senior semantic information, they may do great help to video indexing and video content understanding.
准确提取视频高层语义特征，有助于更好地进行基于内容的视频检索。
Extracting high level features from video accurately benefits the content-based video retrieval.
它是进行基于语义的视频分析的必要前提，也为基于内容的视频处理提供了基础。
It is not only the necessary precondition of the semantic-based video analysis, but also the basis of the content-based video processing.
近年来，面向语义内容的视频检索受到越来越多的关注，它的目的是实现视频的自动语义分析和检索。
In recent years, semantic content-oriented video retrieval attracts more and more attention, and its aim is to achieve the automatic semantic video analysis and retrieval.
当大量的语义概念检测子建立以后，基于这些语义概念的视频检索将是实现面向语义内容的视频检索的有效途径。
When a large number of semantic concept descriptors were build up, semantic concepts based video retrieval would be the effective way to achieve content-oriented video retrieval.
在语义层次上检索视频内容，可以突破“语义鸿沟”，提高视频内容的利用效率。
Retrieving video content in the semantic level can break "semantic gap" and increase the utilization efficiency of video content.
在语义层次上检索视频内容，可以突破“语义鸿沟”，提高视频内容的利用效率。
Retrieving video content in the semantic level can break "semantic gap" and increase the utilization efficiency of video content.
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