FRESH: Fr\'echet Similarity with Hashing
Computational Geometry
2019-06-06 v3 Data Structures and Algorithms
Abstract
This paper studies the -range search problem for curves under the continuous Fr\'echet distance: given a dataset of polygonal curves and a threshold , construct a data structure that, for any query curve , efficiently returns all entries in with distance at most from . We propose FRESH, an approximate and randomized approach for -range search, that leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications. We experimentally compare \fresh to exact and deterministic solutions, and we show that high performance can be reached by suitably relaxing precision and recall.
Keywords
Cite
@article{arxiv.1809.02350,
title = {FRESH: Fr\'echet Similarity with Hashing},
author = {Matteo Ceccarello and Anne Driemel and Francesco Silvestri},
journal= {arXiv preprint arXiv:1809.02350},
year = {2019}
}