在海量图像数据库上的实验结果说明了该算法的有效性。
Experiment results on large image database demonstrate the effectiveness of the proposed algorithm.
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.
用户如何从海量图像数据库中快速而又淮确地检索出需要的图像，是目前研究的一项重要课题。
For users, how to retrieve the image quickly and accurately from the large image database, is an important topic in current research.
如何将信息从海量图像数据库中及时检索出来，是近年来网络信息处理面临的“瓶颈”，已成为国内外研究热点。
How to retrieve relevant images quickly on demand in the enormous digital image databases has become the bottleneck of information processing techniques.
图像是一种直接形象地描述客观世界的信息载体，快速高效的从图像数据库中找到查询图像是海量信息处理所面临的瓶颈。
Image is an information carrier which can describe the objective world directly and vividly. It is bottleneck that the query image is found from the massive image database fast and efficiently.
图像是一种直接形象地描述客观世界的信息载体，快速高效的从图像数据库中找到查询图像是海量信息处理所面临的瓶颈。
Image is an information carrier which can describe the objective world directly and vividly. It is bottleneck that the query image is found from the massive image database fast and efficiently.
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