Using Edit Distance in Point-Pattern Matching
Department of Computer Science, P.O Box 26 (Teollisuuskatu 23)
FIN-00014 University of Helsinki, Finland.
Abstract. Edit distance
is a powerful measure of similarity in string
matching, measuring the minimum amount of
insertions, deletions, and substitutions to convert a
string into another string. This measure is often
contrasted with time warping in speech processing, that measures
how close two trajectories are by allowing compression
and expansion operations on time scale. Time warping can be
easily generalized to measure the similarity
between 1D point-patterns (ascending lists of real values),
as the difference between
points (i) and (i-1) in a point-pattern can be
considered as the value of a
trajectory at the time i. However, we show that edit distance
is more natural choice, and derive a measure
by calculating the minimum amount of space needed to insert
and delete between points to convert a
point-pattern into another. We show that this measure defines
a metric. We also define a substitution operation such that
the distance calculation automatically separates the points
into matching and mismatching points.
The algorithms are based on dynamic programming.
The main motivation for these methods is two and higher
dimensional point-pattern matching, and therefore we
generalize these methods into the 2D case, and show that this
generalization leads to an NP-complete problem.
There is also applications for the 1D case; we discuss shortly
the matching of tree ring sequences