The enhancement of high-dimensional indexing technique is necessary to improve the performance of image database retrieval.
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。
To speed up high-dimensional similarity search efficiency, a novel high-dimensional indexing structure based on dual distance metric (DDM) was proposed.
为了提高高维数据相似查询的效率，提出一种基于双重距离尺度(DDM)的新型高维索引结构。
The splitting strategy for high dimensional data set is important for the performance of the indexing of high-dimensional database.
数据集的划分策略是影响高维数据库索引性能的一个关键因素。
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
低层视觉特征提取、高维数据索引机制和相关反馈方法是面向大规模图像库基于内容检索的三个关键问题。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
In the similarity query processing based on high dimensional indexing, the searching space is usually narrowed down by pruning the inactive subspaces which do not contain any query results.
在基于高维索引技术的相似性查询处理中，通常通过过滤那些不包含任何查询结果的非活动子空间来不断缩减搜索空间。
In the similarity query processing based on high dimensional indexing, the searching space is usually narrowed down by pruning the inactive subspaces which do not contain any query results.
在基于高维索引技术的相似性查询处理中，通常通过过滤那些不包含任何查询结果的非活动子空间来不断缩减搜索空间。
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