期刊論文

標題 A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data
學年 101
學期 2
出版(發表)日期 2013/03/31
作品名稱 A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data
作品名稱(其他語言)
著者 Yen, Shwu-Huey; Hsieh, Ya-Ju
單位 淡江大學資訊工程學系
出版者 Seoul: Korean Society for Internet Information
著錄名稱、卷期、頁數 Transactions on Internet and Information Systems 7(3), pp.459-470
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.
關鍵字 Arbitrary KD-tree (KDA);Feature Point;KD-Tree;Nearest Neighbor (NN);Image Stitching
語言 英文
ISSN 1976-7277
期刊性質 國外
收錄於 SCI;
產學合作
通訊作者 Yen, Shwu-Huey
審稿制度
國別 韓國
公開徵稿
出版型式 ,電子版