淡江大學機構典藏:Item 987654321/59873
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    題名: Applying Multiple KD-Trees in High Dimensional Nearest Neighbor Searching
    作者: Yen, Shwu Huey;Shih, Chao Yu;Li, Tai Kuang;Chang, Hsiao Wei
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: feature matching;nearest neighbor searching (NNS);kd-tree;backtracking;best-bin-first;projection.
    日期: 2010-09
    上傳時間: 2013-06-07 10:47:39 (UTC+8)
    出版者: Sofia: North Atlantic University Union
    摘要: Feature matching plays a key role in many image
    processing applications. To be robust and distinctive, feature vectors
    usually have high dimensions such as in SIFT (Scale Invariant Feature
    Transform) with dimension 64 or 128. Thus, accurately finding the
    nearest neighbor of a high-dimension query feature point in the target
    image becomes essential. The kd- tree is commonly adopted in
    organizing and indexing high dimensional data. However, in searching
    nearest neighbor, it needs many backtrackings and tends to make
    errors when dimension gets higher. In this paper, we propose a
    multiple kd-trees method to efficiently locate the nearest neighbor for
    high dimensional feature points. By constructing multiple kd-trees, the
    nearest neighbor is searched through different hyper-planes and this
    effectively compensates the deficiency of conventional kd-tree.
    Comparing to the well known algorithm of best bin first on kd-tree, the
    experiments showed that our method improves the precision of the
    nearest neighbor searching problem. When the dimension of data is 64
    or 128 (on 2000 simulated data), the average improvement on
    precision can reach 28% (compared under the same dimension) and
    53% (compared under the same number of backtrackings). Finally, we
    revise the stop criterion in backtracking. According to the preliminary
    experiments, this revision improves the precision of the proposed
    method in the searching result
    關聯: International Journal of Circuits, Systems and Single Processing 4(4), pp.153-160
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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