中文摘要：

针对海量图像数据的高维特征索引和查询方法,设计了一个面向云环境的两阶段图像高维特征索引框架,并基于MapReduce机制进行了系统实现。提出了一种基于位置敏感哈希函数的两阶段索引框架,可有效支持高维特征索引的分布式创建;利用MapReduce计算机制,设计和实现了分布式索引构建和查询算法,并集成到非结构化数据管理系统中。实验结果表明,该索引框架的查询速度随着数据规模不断增大呈亚线性增长。

英文摘要：

Aiming at high-dimensional feature indexing and searching method of mass images data in the cloud,a two-phase cloud-oriented image high-dimensional feature indexing framework was designed and was implemented based on MapReduce mechanism.A based Image high-dimensional Feature indexing Framework（LIFF） based on Locality Sensitive Hash（LSH） function was proposed to effectively support the distributed creation of massive high-dimensional feature data in the cloud.Distributed indexing and searching algorithm based on MapReduce framework was designed and implemented,which was integrated into laSQL Unstructured Data Management System（LaUD-MS）.Experimental results showed that query speed on the LIFF index was sub-linear growth with the data scale increased constantly.