A note on the Nagendraprasad-Wang-Gupta thinning algorithm
Pattern Recognition Letters
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
Fully Dynamic Spatial Approximation Trees
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Data Structures and Efficient Algorithms, Final Report on the DFG Special Joint Initiative
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)
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
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
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
Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Similarity Search: The Metric Space Approach
Similarity Search: The Metric Space Approach
| Hi-index | 0.10 |
To speed up similarity based searches many indexing techniques have been proposed in order to address the problem of efficiency. However, most of the proposed techniques do not admit fast insertion of new elements once the index is built. The main effect is that changes in the environment are very costly to be taken into account. In this work, we propose a new technique to allow fast insertions of elements in a family of static tree-based indexes. Unlike other techniques, the resulting index is exactly equal to the index that would be obtained by building it from scratch. Therefore there is no performance degradation in search time. We show that the expected number of distance computations (and the average time complexity) is bounded by a function that grows with log^2(n) where n is the size of the database. In order to check the correctness of our approach some experiments with artificial and real data are carried out.