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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92657


    Title: A KD-Tree-Based Nearest Neighbor Search for Large Quantities of Data
    Authors: Yen, Shwu-Huey;Hsieh, Ya-Ju
    Contributors: 淡江大學資訊工程學系
    Keywords: Arbitrary KD-tree (KDA);Feature Point;KD-Tree;Nearest Neighbor (NN);Image Stitching
    Date: 201303
    Issue Date: 2013-10-21 17:21:13 (UTC+8)
    Publisher: Seoul: Korean Society for Internet Information
    Abstract: 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.
    Relation: Transactions on Internet and Information Systems 7(3), pp.459-470
    DOI: 10.3837/tiis.2013.03.003
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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