多维向量动态索引结构研究

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

Research on Dynamic Indexing Structure for Multi-Dimensional Vectors

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 test results show that(1)the tree creation algorithm proposed is about 10 times faster thanR*-Tree.(2)the effectiveness of retrieval is highly improved.