会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明授权
    • Efficient concurrency control method for high dimensional index structures
    • 高维度索引结构的高效并发控制方法
    • US06480849B1
    • 2002-11-12
    • US09409814
    • 1999-09-30
    • Jang Sun LeeYoung-Kee SongMyung-Joon KimJae Soo YooSeok Il Song
    • Jang Sun LeeYoung-Kee SongMyung-Joon KimJae Soo YooSeok Il Song
    • G06F1730
    • G06F17/30362G06F17/30592Y10S707/99938Y10S707/99953
    • A concurrency control method for a high dimensional index structure that provides efficient concurrency control method for a high dimensional index structure, which performs reinsertion of certain objects to cope with node overflow. The concurrency controlled searching method includes the following steps. First, an entry is obtained from a queue storing the root node and an object relating to the entry is selected. Second, whether a logic sequence number (LSN) of a lower level node is larger than an expected LSN stored in the upper node is determined. Third, the process moves to a neighbor node of the lower level node if the LSN is bigger than an expected LSN stored in the upper node in the second step, selects a relating object, and performs from the second step repeatedly. Fourth, an object of a node of a level corresponding to the lower level node in a reinsertion table is selected when a search on an index tree is finished if the LSN is not bigger than the expected LSN stored in the upper node in the second step.
    • 一种用于高维度索引结构的并发控制方法,为高维度索引结构提供有效的并发控制方法,该方法执行某些对象的重新插入以应对节点溢出。 并发控制搜索方法包括以下步骤。 首先,从存储根节点的队列中获得条目,并且选择与该条目相关的对象。 第二,确定较低级节点的逻辑序列号(LSN)是否大于存储在上级节点中的预期LSN。 第三,如果在第二步中LSN大于存储在上层节点中的预期LSN,则该过程移动到下级节点的邻居节点,选择相关对象,并从第二步重复执行。 第四,当在第二步骤中如果LSN不大于存储在上层节点中的预期LSN的情况下,当索引树上的搜索完成时,选择与重新插入表中的较低级节点相对应的级别的节点的对象 。
    • 4. 发明授权
    • Insertion method in a high-dimensional index structure for content-based image retrieval
    • 用于基于内容的图像检索的高维索引结构中的插入方法
    • US06389424B1
    • 2002-05-14
    • US09429300
    • 1999-10-28
    • June KimDae Young HurJin Soo LeeJae Soo YooSeok Hee Lee
    • June KimDae Young HurJin Soo LeeJae Soo YooSeok Hee Lee
    • G06F1730
    • G06F17/30256Y10S707/99942
    • An insertion method in a high-dimensional index structure for a content-based image retrieval is disclosed, in which a desired image can be efficiently searched when there is formed a high-dimensional image database. In the present invention, the basic properties of the CIR tree are utilized, and at the same time, a splitting algorithm having a superior search efficiency over the conventional CIR tree is employed. Further, an effective standard for choosing lower nodes is provided, and a re-insertion algorithm capable of re-inserting based on a weighted center is employed, thereby forming an ECIR (Extended CIR) as a high-dimensional index structure supporting efficient retrieval performance. That is, a splitting algorithm for the branch nodes and the terminal nodes are adopted so as to improve the efficiency when carrying out the search and insertion. The re-insertion objects are chose based on the weighted center when the nodes overflow. According to the present invention, the images can be efficiently searched when an image information containing many feature dimensions is formed into a database.
    • 公开了一种用于基于内容的图像检索的高维索引结构中的插入方法,其中当形成高维图像数据库时可以有效地搜索期望的图像。 在本发明中,利用了CIR树的基本特性,同时采用了比常规CIR树具有更好的搜索效率的分割算法。 此外,提供了用于选择较低节点的有效标准,并且采用能够基于加权中心重新插入的重新插入算法,从而形成作为支持有效检索性能的高维索引结构的ECIR(扩展CIR) 。 也就是说,采用分支节点和终端节点的分割算法,以便在进行搜索和插入时提高效率。 当节点溢出时,基于加权中心选择重新插入对象。 根据本发明,当将包含许多特征维度的图像信息形成数据库时,可以有效地搜索图像。