Take high level semantic information and low level picture visual characters as design patent characters, the two kinds of information cut down each other's shortage.
特征提取采用底层信息和高层语意相结合的方法，弥补了相互的不足；
Our work also includes the overall design of the system framework and the design of high-level semantic retrieval module.
设计工作还包括整体系统框架的设计和高级语义检索模块的设计等。
This paper presented two key problems to shorten "semantic gap" distance between low-level visual features and high-level semantic features.
为了缩短介于低层视觉特征与高层语义特征之间的“语义鸿沟”距离，提出了急需解决的两大关键问题。
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。
As the text with high-level semantic feature and plays an important role on understanding, indexing and retrieval image content.
由于文字具有高级语义特征，对图片内容的理解、索引、检索具有重要作用，因此，研究图片文字提取具有重要的实际意义。
How to annotate the image semantic automatically in order to across semantic gap between the feature and the high-level semantic of the image is a difficult problem.
如何跨越图像底层特征和高层语义之间的语义鸿沟，使机器自动的实现图像语义标注更是研究的难点。
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。
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。
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。
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