FRESH Frechet Similarity With Hashing
Ceccarello Matteo, Driemel Anne, Silvestri Francesco. Proc. of Algorithms and Data Structures Symposium 2018
This paper studies the \(r\)-range search problem for curves under the continuous Fr'echet 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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