1.CBIR(Content-Based Image Retrieval) is a very important area of graph analyze and also the difficult of the search engine technology.
基于内容的图像检索（CBIR）是图像分析的一个的重要研究领域，也是目前搜索引擎技术的难点。
2.The Content-Based Image Retrieval (CBIR) has become one of the hot research areas in image domain for large image database.
随着大规模图像数据库的产生，基于内容的图像检索技术成为图像领域研究的热点问题之一。
3.In the end of this paper, a CBIR system for testing retrieval algorithms is developed, which is an experimental frame system.
本文设计并开发了算法测试平台及基于内容的图像检索实验原型系统，是一个实验性的框架系统。
4.In Content-Based Image Retrieval (CBIR) area, features extraction and matching are the key technique for image retrieval.
在基于内容的图像检索中，图像特征的提取和特征匹配是图像检索的核心。
5.CBIR system for testing retrieval algorithms is developed to test the algorithm proposed by this dissertation.
实现了一个小型的图像检索原型系统，用以测试论文提出的算法。
6.Aiming at the 72-dimensional HSV color feature in content-based image retrieval(CBIR), this paper proposes a new dimension reduction idea.
针对图像的72维HSV颜色特征，提出了一种新的降维方法。
7.CBIR system r is mostly according as image vision character to scale the comparability between images.
图像的视觉特征是CBIR系统用来衡量图像间相似性的主要依据。
8.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。
9.CBIR is a gradually precision process. There is a feature adjustment and rematch process in it.
基于内容的查询和检索是一个逐步求精的过程，存在一个特征调整、重新匹配的循环过程。
10.A new approach that uses classical text retrieval themes for CBIR is proposed.
提出了一种利用文本检索技术进行基于内容的图像检索的新方法。