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

9901636 Samet，哈南大学计算机科学实验博士后：增量最近邻算法在多媒体数据库和医疗应用中的相似性检索相似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。讨论了两种现有的处理相似搜索的方法。第一种是基于到低维几何空间的映射(使用R-树等数据结构)，而第二种是直接基于距离对对象进行索引(使用M-树等数据结构)。对于这两种方法，最近邻搜索的新增量算法的开发正在进行中。算法将具有按相似性顺序逐个报告其结果的特性，而生成每个新的结果对象所花费的努力尽可能少。在所需邻居的数量预先未知的情况下，增量方面提供了显著的好处。这些算法在多媒体数据库、医疗应用以及其他领域的应用正在被研究中。

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.