本文提出了一种有效的支持海量图像数据库Q BE查询的聚类索引算法。
This paper proposes an indexing algorithm of clustering which supports QBE image retrieval for large image databases.
多维索引技术是基于内容检索的图像数据库的关键技术。
Multidimensional indexing technology is the key technology of content-based retrieval in image database.
为了提高图像数据库的检索效率，必须提高高维索引的效率。
The enhancement of high-dimensional indexing technique is necessary to improve the performance of image database retrieval.
相对而言，IISE致力于建立简洁的索引结构，旨在对大规模的图像数据库快速地搜索相似的图像。
In contrast, IISE does great efforts on building compact index structures, for querying similar images with a fast speed on large scale of image database.
另外提出一种图像比对搜索方法，即利用最新的分形图像处理和索引技术建立图像特征数据库。
In this paper, it also propose a new method of search engine of image compare, it establishes image feature database by new fractal image processing and index.
另外为提高了图像的检索速度，采用K均值聚类索引建立数据库。
To improve the speed of image search, K-means Clustering is used to create the image database.
传统的图像索引方法从图像数据库中按照关键字或号码，和按分类描述来检索引图像。
In traditional method of image retrieval searches images according to keys, number and sort describe from image database.
传统的图像索引方法从图像数据库中按照关键字或号码，和按分类描述来检索引图像。
In traditional method of image retrieval searches images according to keys, number and sort describe from image database.
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