【摘要】 多维向量的索引技术是多媒体数据库系统中的关键技术之一.集中研究基于向量空间模型的动态索引结构,以解决在图像数据库系统中按内容快速检索图像的对象问题.在分析研究R-Tree和R＊-Tree的基础上,提出了ER-Tree动态索引结构.该索引树用超球体划分多维向量空间,以有利于计算最近邻;吸取R＊-Tree树的重插技术,以增强索引树对数据集整体特征的表达能力,从而提高检索效率;通过引入插入安全点和删除安全点概念,有效地提高建树的效率.同时,给出了基于该结构的特征向量插入算法.实验结果表明,所提出的索引结构建树的效率比R＊-Tree提高10倍,检索的有效性也有明显的提高.更多还原

【Abstract】 Multi-Dimentional vectors indexing is one of the key technologies in multimedia database systems. This paper focuses on the dynamic indexing structure based on the vector space. An ER-Tree indexing structure is proposed which is efficient for medium and high dimensional vectors. In the ER-Tree, the efficiency of retrieval could be improved by the ways of partition of the vector space with hyper-sphere and R*-Tree’s re-insert technology introduced to enhance ER-Tree’s capability to represent the data-sets’ features. By introducing safe-inserted-node and safe-deleted-node concept into the structure, the performance of tree creation is reenforced. The algorithms are also given for the creation of ER-Tree. The empirical test results show that (1) the tree creation algorithm proposed is about 10 times faster than R*-Tree. (2) the effectiveness of retrieval is highly improved.更多还原