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 ...

• Type: Journal
• Year: 2011
• Where: MMM
• Authors: Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Ying He 0001
• Keywords: Based Image Retrieval, KD Tree, MMM 2011, Multimedia, Retrieval Quality