KD-Tree Based Nearest Neighbor Search for Large Quantity Data
碩士 === 淡江大學 === 資訊工程學系碩士班 === 100 === Finding nearest neighbors without training in advance has many applications, such as image mosaic, image matching, image retrieval, and image stitching. When the quantity of data is huge and dimension is high, how to efficiently find the nearest neighbor (NN) be...
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ndltd-TW-100TKU053920082016-04-04T04:17:02Z http://ndltd.ncl.edu.tw/handle/96185347999942433240 KD-Tree Based Nearest Neighbor Search for Large Quantity Data 基於KD-TREE的高資料量最近鄰居搜尋法 Ya-Ju Hsieh 謝雅如 碩士 淡江大學 資訊工程學系碩士班 100 Finding nearest neighbors without training in advance has many applications, such as image mosaic, image matching, image retrieval, and image stitching. When the quantity of data is huge and dimension is high, how to efficiently find the nearest neighbor (NN) becomes very important. In this article, we propose a variation of the KD-tree, Arbitrary KD-tree (KDA) which build tree without evaluate variances. Multiple KDA not only can be built efficiently it also processes an independent tree structures when data is large. Tested by extended synthetic databases and real-world SIFT data, we concluded that KDA method has advantages of satisfying accuracy performance in NN problem as well as computation efficiency. Shwu-Huey Yen 顏淑惠 2012 學位論文 ; thesis 62 zh-TW |
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碩士 === 淡江大學 === 資訊工程學系碩士班 === 100 === Finding nearest neighbors without training in advance has many applications, such as image mosaic, image matching, image retrieval, and image stitching. When the quantity of data is huge and dimension is high, how to efficiently find the nearest neighbor (NN) becomes very important. In this article, we propose a variation of the KD-tree, Arbitrary KD-tree (KDA) which build tree without evaluate variances. Multiple KDA not only can be built efficiently it also processes an independent tree structures when data is large. Tested by extended synthetic databases and real-world SIFT data, we concluded that KDA method has advantages of satisfying accuracy performance in NN problem as well as computation efficiency.
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author2 |
Shwu-Huey Yen |
author_facet |
Shwu-Huey Yen Ya-Ju Hsieh 謝雅如 |
author |
Ya-Ju Hsieh 謝雅如 |
spellingShingle |
Ya-Ju Hsieh 謝雅如 KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
author_sort |
Ya-Ju Hsieh |
title |
KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
title_short |
KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
title_full |
KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
title_fullStr |
KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
title_full_unstemmed |
KD-Tree Based Nearest Neighbor Search for Large Quantity Data |
title_sort |
kd-tree based nearest neighbor search for large quantity data |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/96185347999942433240 |
work_keys_str_mv |
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