Normalized weighted Levensthein distance and triangle inequality in the context of similarity discrimination of bilevel images

This work shows that the weighted Levensthein distance under normalization satisfies the triangle inequality, not unconditionally, but under the hypothesis of practical occurrence. This characteristic makes the normalized weighted Levensthein distance a good candidate as a string distance for shape...

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Bibliographic Details
Published in:Pattern recognition letters 1996-05, Vol.17 (5), p.431-436
Main Authors: Cortelazzo, G., Deretta, G., Mian, G.A., Zamperoni, P.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Contour coding
Distance
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Shape analysis
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Summary:This work shows that the weighted Levensthein distance under normalization satisfies the triangle inequality, not unconditionally, but under the hypothesis of practical occurrence. This characteristic makes the normalized weighted Levensthein distance a good candidate as a string distance for shape similarity discrimination of bilevel images. It is shown that a string distance is ideal for such a role when it is a normalized metric.
ISSN:0167-8655
1872-7344
DOI:10.1016/0167-8655(95)00123-9