Data structures and algorithms for nearest neighbor search in general metric spaces
Peter N. Yianilos
- pp 311-321
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TLDR
The up-tree (vantage point tree) is introduced in several forms, together‘ with &&ciated algorithms, as an improved method for these difficult search problems in general metric spaces.Abstract:
We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation 1s very high. Also relevant are high-dimensional Euclidian settings in which the distribution of data is in some sense of lower dimension and embedded in the space. The up-tree (vantage point tree) is introduced in several forms, together‘ with &&ciated algorithms, as an improved method for these difficult search nroblems. Tree construcI tion executes in O(nlog(n i ) time, and search is under certain circumstances and in the imit, O(log(n)) expected time. The theoretical basis for this approach is developed and the results of several experiments are reported. In Euclidian cases, kd-tree performance is compared.read more
Citations
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What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images
TL;DR: This work compares and evaluates a number of nearest neighbors algorithms for speeding up computer vision algorithms, and indicates that vantage point trees, which are not well known in the vision community, generally offer the best performance.
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Similarity search without tears: the OMNI-family of all-purpose access methods
TL;DR: A family of metric access methods that are fast and easy to implement on top of existing access methods, such as sequential scan, R-trees and Slim-tree, to elect a set of objects as foci and gauge all other objects with their distances from this set.
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