Then, the key technique in CBIR is introduced.
然后介绍了基于内容图像检索的关键技术。
Finally the direction of future development in CBIR has been discussed.
最后对图像检索技术的发展趋势进行了探讨。
The extraction of semantic region is significant for the computer vision and CBIR.
图像中语义区域的提取对于计算机视觉、CBIR等都具有重要的意义。
Relevence feedback is used for CBIR in order to embed user model into image search.
为了把用户模型嵌入到图像检索系统中，基于内容的图像检索领域引入了相关反馈机制。
In the CBIR system, extraction feature and similarity matching become very important.
在CBIR系统中，图像的特征提取和相似度匹配非常重要。
How to describe image characters efficiently and accurately is a core problem in CBIR.
如何有效准确的表达图像特征是基于内容的图像检索技术的核心问题。
In recent years, the content-based image retrieval (CBIR) system is a hot research topic.
近年来，基于内容的图像检索系统(CBIR)是一个热门的研究话题。
The last part proved effectiveness of CBIR system based on relevance feedback technology through an experiment.
第五部分通过对实验结果的分析和总结，证明引入相关反馈机制对CBIR系统性能的优化和改善。
Current CBIR systems generally make use of lower-level features like color, texture, shape and space relationship.
传统的图像特征提取方法，基本上是围绕图像的颜色、纹理、形状和空间关系来展开的。
To access these image databases automatically and on demand requires the system of content-based image retrieval (CBIR).
实现基于内容的图象检索系统的关键问题是实现图象的语义分割。
The color spatial distribution density can provide the color spatial distribution information for CBIR(Content Based Image Retrieval).
在基于内容的图像检索中，颜色的空间分布密度能提供颜色在空间的分布信息。
In this article, based on the introduction of framework of CBIR with relevance feedback, a real image retrieval system was constructed.
本文详细介绍了基于相关性反馈技术的图像检索系统框架。
The principal research of content based image retrieve (CBIR) includes two aspects: visual feature representation and similarity measurement.
基于内容的图像检索(CBIR)技术的研究主要包括两个方面：可视化特征提取和相似性度量。
CBIR is an image retrieval technology, which synthesizes various visual features in digital image, such as color, textual, and shapes features.
基于内容的图像检索是一种利用图像的颜色、纹理、形状等视觉特征进行图像检索的技术。
The methods of CBIR retrieve the images using the characteristics of themselves, such as color, shape, texture and the space position relations.
基于内容的图像检索技术可以克服这些弊端，它在商标检索领域得到了非常广泛的应用。
By using a sorting assessment method, this paper assesses and compares the algorithms, the CBIR system proves to run well with a large image library.
通过该系统，借助排序的评价方法，对本文的基于纹理特征的图象检索算法进行了评价。
In this paper, the methods for CBIR is based on color co-occurrence matrix-a new conception which proposed on the basis of grey level co-occurrence matrix.
介绍了一种基于色彩共生矩阵提取颜色-纹理特征的图像检索方法。
One of the key technologies in CBIR system is the image semantic segmentation. This paper surveys the techniques for image semantic segmentation and class…
该文分六类对现有的图象语义分割技术进行了全面的总结，为进一步研究基于内容的图象检索技术奠定了基础。
However, due to the complexity of the information, the lack of standard for the description of image content induces low interoperability among CBIR systems.
然而由于信息的复杂多样，图像的内容描述方法缺乏规范，导致了基于内容的图像检索系统中存在通用性差的问题。
According to that, the paper expatiates on key technologies used in CBIR researches, such as feature extracting, similarity measuring, and relevance feedback, etc.
面对这种研究现状，本文详细分析了基于内容的图像检索的各种特征提取方法、相似性度量方法以及相关反馈技术等。
Content based image retrieval (CBIR) has been an active research area, however, the achievements in image representation and similarity measurement are not satisfying.
基于内容的多媒体信息检索是当前世界的研究热点，然而在图像内容表示及其相似性度量这两个关键问题上取得的进展还不能令人满意。
At first, we introduce the current research situation of CBIR (Content-based image retrieval) both at home and abroad, basic theories, inquiry ways and application fields.
首先介绍了国内外基于内容的图象检索系统的研究现状，基本原理，查询方式以及应用领域。
We do some researches on the algorithm of CBIR, and pay more attention on the global feature (including color, edge and texture feature) extraction and matching algorithms.
对基于内容的图象信息检索算法作了研究。重点阐述了对颜色、边缘、纹理等全局特征的提取与匹配算法。
Specifically, we make the following contributions:(1) Introduce CBIR technology to product design patents retrieval, establish a new method of product design patents retrieval.
将CBIR技术引入到外观设计专利检索中，开创了一种全新的检索思路；
Traditional techniques of CBIR try to retrieve images through analyzing the similarity of image visual features, but CBIR cannot meet the requirements of semantic image retrieval.
传统CBIR技术试图通过分析图像视觉特征的相似性来检索图像，这不能满足普通人按语义检索图像的需求。
Because of the "semantic gap" which is often encountered in the image data, traditional CBIR technology cant deal with the problem of image retrieval in digital libraries sometimes.
由于图像数据中普遍存在的“语义鸿沟”问题，传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。
Secondly, based on the key technology of CBIR researching, a local CBIR module is implemented in this thesis, which constructs a general frame for the system of image search engine.
其次，本文研究了图像相似性匹配CBIR关键技术，并实现了本地CBIR检索模块，为图像搜索引擎原型系统构建起了总体框架。
Secondly, based on the key technology of CBIR researching, a local CBIR module is implemented in this thesis, which constructs a general frame for the system of image search engine.
其次，本文研究了图像相似性匹配CBIR关键技术，并实现了本地CBIR检索模块，为图像搜索引擎原型系统构建起了总体框架。
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