A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
The VLDB Journal — The International Journal on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A log square average case algorithm to make insertions in fast similarity search
Pattern Recognition Letters
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Similarity search is a widely employed technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. However, the majority of the proposed techniques are static, that is, a fixed training set is used to build up the index. This characteristic becomes a major problem when these techniques are used in dynamic (interactive) systems. In these systems updating the training set is necessary to improve its performance. In this work, we explore the surprising efficiency of a naïve algorithm that allows making incremental insertion in a previously known index: the MDF-tree.