公开(公告)号：KR1020030006638A

公开(公告)日：2003-01-23

申请号：KR1020010042482

申请日：2001-07-13

Applicant: 한국전자통신연구원

Inventor： 박수준 ,  장재우 ,  김현진 ,  박성희 ,  장명길 ,  박상규 ,  한성근

IPC: G06F17/30

Abstract: PURPOSE: A cell based high dimensional data indexing system and method is provided to index high dimensional data based on a cell for preventing a lowering of a search efficiency in searching for high dimensional data. CONSTITUTION: The method comprises several steps. First, an N dimensional feature vector is extracted from a multimedia object via a feature vector extractor(801). A distance signature is generated via a signature generation module by using a distance between a signature on the feature vector and a cell center(802). One signature is generated by concatenating the feature vector signature and the distance signature(803), and then is stored at a signature database(804). At the same time, the feature vector is stored at a feature vector database(805). A user can perform a search operation on the stored feature vectors by using various queries like a point query, a range query or k-nearest query(806).

Abstract translation: 目的：提供一种基于单元的高维数据索引系统和方法，用于根据单元索引高维度数据，以防止在搜索高维数据时降低搜索效率。 构成：该方法包括几个步骤。 首先，经由特征向量提取器（801）从多媒体对象提取N维特征向量。 通过使用特征向量的签名与小区中心之间的距离，通过签名生成模块生成距离签名（802）。 通过连接特征向量签名和距离签名（803）生成一个签名，然后存储在签名数据库（804）。 同时，特征向量存储在特征向量数据库（805）。 用户可以通过使用诸如点查询，范围查询或k-最近查询（806）的各种查询对所存储的特征向量执行搜索操作。

公开(公告)号：KR100446639B1

公开(公告)日：2004-09-04

申请号：KR1020010042482

申请日：2001-07-13

Applicant: 한국전자통신연구원

Inventor： 박수준 ,  장재우 ,  김현진 ,  박성희 ,  장명길 ,  박상규 ,  한성근

IPC: G06F17/30

Abstract: PURPOSE: A cell based high dimensional data indexing system and method is provided to index high dimensional data based on a cell for preventing a lowering of a search efficiency in searching for high dimensional data. CONSTITUTION: The method comprises several steps. First, an N dimensional feature vector is extracted from a multimedia object via a feature vector extractor(801). A distance signature is generated via a signature generation module by using a distance between a signature on the feature vector and a cell center(802). One signature is generated by concatenating the feature vector signature and the distance signature(803), and then is stored at a signature database(804). At the same time, the feature vector is stored at a feature vector database(805). A user can perform a search operation on the stored feature vectors by using various queries like a point query, a range query or k-nearest query(806).

Abstract translation: 目的：提供一种基于单元格的高维数据索引系统和方法，用于基于单元索引高维数据，以防止在搜索高维数据时降低搜索效率。 构成：该方法包括几个步骤。 首先，经由特征向量提取器（801）从多媒体对象提取N维特征向量。 经由签名生成模块通过使用特征向量上的签名与小区中心之间的距离来生成距离签名（802）。 通过连接特征向量签名和距离签名生成一个签名（803），然后存储在签名数据库（804）。 同时，特征矢量被存储在特征矢量数据库（805）。 用户可以通过使用诸如点查询，范围查询或k-最近查询（806）的各种查询来对所存储的特征向量执行搜索操作。