An Efficient Indexing Scheme Based on Linked-Node m-Ary Tree Structure

  • The-Anh Pham
  • Sabine Barrat
  • Mathieu Delalandre
  • Jean-Yves Ramel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


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.


Image Indexing Locality-Sensitive Hashing Clustering Trees 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • The-Anh Pham
    • 1
  • Sabine Barrat
    • 1
  • Mathieu Delalandre
    • 1
  • Jean-Yves Ramel
    • 1
  1. 1.PolytechToursToursFrance

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