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2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 3392-3399
You Jia , Key Laboratory of Machine Perception, Peking University
Jingdong Wang , Microsoft Research Asia
Gang Zeng , Key Laboratory of Machine Perception, Peking University
Hongbin Zha , Key Laboratory of Machine Perception, Peking University
Xian-Sheng Hua , Microsoft Research Asia
ABSTRACT
In this paper, we attempt to scale up the kd-tree indexing methods for large-scale vision applications, e.g., indexing a large number of SIFT features and other types of visual descriptors. To this end, we propose an effective approach to generate near-optimal binary space partitioning and need low time cost to access the nodes in the query stage. First, we relax the coordinate-axis-alignment constraint in partition axis selection used in conventional kd-trees, and form a partition axis with the great variance by combining a few coordinate axes in a binary manner for each node, which yields better space partitioning and requires almost the same time cost to visit internal nodes during the query stage thanks to cheap projection operations. Then, we introduce a simple but very effective scheme to guarantee the partition axis of each internal node is orthogonal to or parallel with those of its ancestors, which leads to efficient distance computation between a query point and the cell associated with each node and yields fast priority search. Compared with the conventional kd-trees, our approach takes a little more tree construction time, but obtains much better nearest neighbor search performance. Experimental results on large scale local patch indexing and image search with tiny images show that our approach outperforms the state-of-the-art kd-tree based indexing methods.
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CITATION

H. Zha, X. Hua, J. Wang, Y. Jia and G. Zeng, "Optimizing kd-trees for scalable visual descriptor indexing," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, CA, USA, 2010, pp. 3392-3399.
doi:10.1109/CVPR.2010.5540006
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