Selecting episode representation frame is one of the important processes in video semantic analysis and content-based video retrieval.
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。
A novel automatic image annotation approach is proposed to bridge the semantic gap of content-based image retrieval.
针对图像检索中的语义鸿沟问题，提出了一种新颖的自动图像标注方法。
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.
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。
In content-based image retrieval systems, the inconsistency between image low-level features and the concept of high-level expressed by images lead to system semantic gap problem.
在基于内容的图像检索系统中，图像低层特征和图像所表达高层概念之间的不一致性导致系统出现语义鸿沟问题。
The same time, it raise a new structure of system of content based image indexing and retrieval which can adapt oneself for adding successful semantic users did to semantic database.
同时提出一种基于内容的图像标引与检索系统结构，能自适应的在图像语义库中添加较为成功的语义表述。
This paper introduced the technology of content based image indexing and retrieval concisely. It propose to increase high level semantic describe of image to approach visual sense of human being.
本文通过对现有基于内容图像标引及检索技术的简要介绍，提出应在现有系统中增加图像的高层语义概念描述，以更接近于人的视觉效果。
The contents of image include low level visual features and high level semantic in image retrieval based on content.
在基于内容的图像检索中，图像的内容包括图像的低层视觉特征和高层语义。
Research on Semantic Extraction of Content-based Video Retrieval;
研究语言表达方式对科学概念语义提取的影响。
Research on Semantic Extraction of Content-based Video Retrieval;
研究语言表达方式对科学概念语义提取的影响。
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