We study data structures for storing a set of polygonal curves in {\rm R}^d such that, given a query curve, we can efficiently retrieve similar curves from the set, where similarity is measured using the discrete Fréchet distance or the dynamic time warping distance.
Mar 11, 2017
Abstract. We study data structures for storing a set of polygonal curves in IRd such that, given a query curve, we can efficiently retrieve similar curves ...
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Abstract. We study data structures for storing a set of polygonal curves in R^d such that, given a query curve, we can efficiently retrieve similar curves from ...
Overview. Experimental implementation of the paper 'Locality-sensitive hashing of curves' published by A. Driemel and F. Silvestri.
An LSH function aims to place similar values into the same buckets. There is no single approach to hashing in LSH. Indeed, they all share the same 'bucket ...
Jun 1, 2017 · The basic LSH has an approximation factor that is linear in the number of vertices that a curve can have. 3.1 Algorithm. We use a randomly ...
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same buckets with high probability.
Missing: Curves. | Show results with:Curves.
Locality Sensitive Hashing (LSH) is one of the most popular approximate nearest neighbors search (ANNS) methods. At its core, it is a hashing function that ...
Missing: Curves. | Show results with:Curves.
The seminal work on Locality-Sensitive Hashing (LSH) (Gionis et al., 1999) uses simple random projections for such mapping.
Jul 30, 2023 · LSH is a technique that efficiently approximates similarity search by reducing the dimensionality of data while preserving local distances between points.