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• W255331330 abstract "Feature matching plays a key role in many image processing applications. The matching efficacy is crucially determined by the description of features. To be robust and distinctive, feature vectors usually have high dimensions. Thus accurately finding the nearest neighbor of a high dimensional query point in the target image becomes essential. In this paper, we propose a multiple kd-trees method to locate the nearest neighbor for high dimension feature points. First, we project feature points to three hyper-planes corresponding coordinate axes that are with the first three greatest variances. Secondly, for points projected on each splitting hyper-plane, two kd-trees are built that one is the conventional kd-tree and the other has a first split on the hyper-plane with the second largest variance. Thus in total six kd-trees are built to compensate the deficiency of projection may have. Although our method requires a longer time in tree construction, it is still quite efficient (not more than 0.62 seconds for 1000 data of dimension 50). The experiment showed that our method improves the precision of the nearest neighbor searching problem. When the dimension of data is 64 or 128, the average improvement on precision can reach 28% (dimension fixed) and 53% (number of backtracking fixed)." @default.
• W255331330 created "2016-06-24" @default.
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• W255331330 date "2010-08-20" @default.
• W255331330 modified "2023-09-23" @default.
• W255331330 title "Nearest neighbor searching in high dimensions using multiple KD-trees" @default.
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