Experiment results on large image database demonstrate the effectiveness of the proposed algorithm.
在海量图像数据库上的实验结果说明了该算法的有效性。
For users, how to retrieve the image quickly and accurately from the large image database, is an important topic in current research.
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。
So research on how to organize, manage and retrieval the large WEB image database has great value to future Internet service.
因此，研究如何有效地组织、管理和检索大规模的WEB图像数据库，对未来互联网服务具有重要的理论和应用价值。
As the volume of image database grows, it is urged to work over high effective index technique to support fast similarity search in very large databases.
图像数据库容量的增长，迫切需要研究高效的索引技术来支持快速相似性检索的要求。
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.
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。
Evaluate two famous commercial face recognition systems and four popular face recognition methods based on the newly created CAS-PEAL large-scale Chinese face image database.
介绍了四种著名的人脸识别方法及其两个著名的商业人脸识别系统在CAS-PEAL大规模人脸图像数据上的评测情况，借此分析了人脸识别研究和开发的现状。
Evaluate two famous commercial face recognition systems and four popular face recognition methods based on the newly created CAS-PEAL large-scale Chinese face image database.
介绍了四种著名的人脸识别方法及其两个著名的商业人脸识别系统在CAS-PEAL大规模人脸图像数据上的评测情况，借此分析了人脸识别研究和开发的现状。
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