An efficient tree structure for indexing feature vectors

✩ The-Anh Pham (1) , Sabine Barrat (1) , Mathieu Delalandre (1) , Jean-Yves Ramel (1)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

Keywords: Exact nearest neighbour search Approximate nearest neighbour search Feature indexing Randomized KD-trees Randomized clustering trees a b s t r a c t This paper addresses the problem of feature indexing in feature vector space. A linked-node m-ary tree (LM-tree) structure is presented to quickly produce the queries for an approximate and exact nearest neighbour search. Three main contributions are made, which can be attributed to the proposed LM-tree. 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. Extensive experiments were performed to study the behaviour of the proposed indexing algorithm. These experimental results showed that the proposed algorithm provides a significant improvement in the search performance for the tasks of both exact and approximate nearest neighbour (ENN/ANN) searches.

Domaines: Traitement du texte et du document; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement du signal et de l'image [eess.SP]; Imagerie médicale; Interface homme-machine [cs.HC]

✩ The-Anh Pham, Sabine Barrat, Mathieu Delalandre, Jean-Yves Ramel. An efficient tree structure for indexing feature vectors. Pattern Recognition Letters, 2015, Pattern Recognition Letters, 55, pp.42-50. ⟨10.1016/j.patrec.2014.08.006⟩. ⟨hal-01159623⟩