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CISE Postdoctoral Rsch Associates in Experimental Computer Science: Incremental Nearest Neighbor Algorithms for Similarity Retrivai in Multimedia Databases and Medical Applications

CISE 实验计算机科学博士后 Rsch 助理：多媒体数据库和医学应用中相似性检索的增量最近邻算法

基本信息

批准号：
9901636
负责人：
Hanan Samet
金额：
$ 6.6万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1999
资助国家：
美国
起止时间：
1999-08-15 至 2003-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9901636&HistoricalAwards=false
关键词：
CISE Postdoctoral Rsch Associates Experimental

项目摘要

9901636Samet, HananUniversity of MarylandCISE Postdoctoral Associates in Experimental Computer Science: IncrementalNearest Neighbor Algorithms for Similarity Retrieval in MultimediaDatabases and Medical ApplicationsSimilarity search is a very important operation in multimedia databases and other database applications involving complex objects. The similarity between two objects is represented with a distance metric. Two existing approaches for handling similarity search are addressed. The first is based on mapping to low-dimensional geometric space (making use of data structures such as the R-tree), while the second directly indexes the objects based on distances (making use of data structures such as the M-tree). For both approaches, the development of new incremental algorithms for nearest neighbor search are being investigated. The algorithms will have the property of reporting their result one-by-one in order ofsimilarity, with as little effort as possible expended to produce each new result object. The incremental aspect provides significant benefits in situations when the number of desired neighbors is unknown in advance. The application of the algorithms to problems in fields such as multimedia databases, medical application, as well as others, are being investigated.

9901636Samet，Hanan马里兰大学CISE实验计算机科学博士后研究员：多媒体数据库和医学应用中相似性检索的增量最近邻算法相似性搜索是多媒体数据库和其他涉及复杂对象的数据库应用中非常重要的操作。两个对象之间的相似性用距离度量来表示。处理相似性搜索的两个现有的方法。第一种是基于映射到低维几何空间（利用数据结构，如R树），而第二种直接索引的对象基于距离（利用数据结构，如M树）。对于这两种方法，新的增量算法最近邻搜索的发展正在调查。算法将具有按照相似性顺序逐个报告结果的特性，尽可能少地花费精力来产生每个新的结果对象。增量方面在所需邻居的数量事先未知的情况下提供了显著的益处。该算法的应用领域，如多媒体数据库，医疗应用，以及其他问题，正在调查中。