期刊論文
| 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 | en |
| 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 | 是 |
| 國別 | KOR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92657 ) |