An efficient indexing scheme based on linked-node m-ary tree structure

Abstract

Fast nearest neighbor search is a crucial need for many recognition systems. Despite the fact that a large number of indexing algorithms have been proposed in the literature, few of them (e.g., randomized KD-trees, hierarchical K-means tree, randomized clustering trees, and LHS-based schemes) have been well validated on extensive experiments to give satisfactory performance on specific benchmarks. In this work, we propose a linked-node m-ary tree (LM-tree) algorithm, which works really well for both exact and approximate nearest neighbor search. The main contribution of the LM-tree is three-fold. First, a new polar-space-based method of data decomposition is presented to construct the LM-tree. Second, a novel pruning rule is proposed to efficiently narrow down the search space. Finally, a bandwidth search method is introduced to explore the nodes of the LM-tree. Our experiments, applied to one million 128-dimensional SIFT features and 250000 960-dimensional GIST features, showed that the proposed algorithm gives the best search performance, compared to the aforementioned algorithms. © 2013 Springer-Verlag.