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2011
Springer

Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval

7 years 10 months ago
Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval
Abstract. Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree) usually are only efficient for low dimensional data, but often perform no better than a simple exhaustive linear search when the number of dimensions is large enough. Recently, approximate NN search techniques have been proposed for highdimensional search, such as Locality-Sensitive Hashing (LSH), which adopts some random projection approach. Motivated by similar idea, in this paper, we propose a new high dimensional NN search method, called Randomly Projected kd-Trees (RP-kd-Trees), which is to project data points into a lower-dimensional space so as to exploit the advantage of multiple kd-trees over low-dimensional data. Based on the proposed framework, we present an enhanced RP-kd-Trees scheme by applying distance metric learning techniques. We conducted extensive empirical studies on CBIR, which showed ...
Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Y
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where MMM
Authors Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Ying He 0001
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