The rapid increase of image resources makes it necessary to develop technology for efficient image indexing and retrieval.
图像资源的迅速增长使我们面临新的挑战，迫使人们对其索引与检索技术进行深入研究。
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。
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
低层视觉特征提取、高维数据索引机制和相关反馈方法是面向大规模图像库基于内容检索的三个关键问题。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
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。
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。
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