FRESH: Fréchet Similarity with Hashing

This paper studies the r-range search problem for curves under the continuous Frechet distance: given a dataset S of n polygonal curves and a threshold \(r>0\), construct a data structure that, for any query curve q, efficiently returns all entries in S with distance at most r from q. We propose FRESH, an approximate and randomized approach for r-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.

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