A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data

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题名:	A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data
作者:	Yen, Shwu-Huey;Hsieh, Ya-Ju
贡献者:	淡江大學資訊工程學系
关键词:	Arbitrary KD-tree (KDA);Feature Point;KD-Tree;Nearest Neighbor (NN);Image Stitching
日期:	2013-03
上传时间:	2013-10-21 17:21:13 (UTC+8)
出版者:	Seoul: Korean Society for Internet Information
摘要:	The discovery of nearest neighbors, without training in advance, has many applications, such as the formation of mosaic images, image matching, image retrieval and image stitching. When the quantity of data is huge and the number of dimensions is high, the efficient identification of a nearest neighbor (NN) is very important. This study proposes a variation of the KD-tree - the arbitrary KD-tree (KDA) - which is constructed without the need to evaluate variances. Multiple KDAs can be constructed efficiently and possess independent tree structures, when the amount of data is large. Upon testing, using extended synthetic databases and real-world SIFT data, this study concludes that the KDA method increases computational efficiency and produces satisfactory accuracy, when solving NN problems.
關聯:	Transactions on Internet and Information Systems 7(3), pp.459-470
DOI:	10.3837/tiis.2013.03.003
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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