Polynomial-time metrics for attributed trees.

Andrea Torsello; Dzena Hidović-Rowe; Marcello Pelillo

doi:10.1109/tpami.2005.146

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Polynomial-time metrics for attributed trees.

Andrea Torsello·Dzena Hidović-Rowe·Marcello Pelillo

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Abstract

We address the problem of comparing attributed trees and propose four novel distance measures centered around the notion of a maximal similarity common subtree. The proposed measures are general and defined on trees endowed with either symbolic or continuous-valued attributes and can be applied to rooted as well as unrooted trees. We prove that our measures satisfy the metric constraints and provide a polynomial-time algorithm to compute them. This is a remarkable and attractive property, since the computation of traditional edit-distance-based metrics is, in general, NP-complete, at least in the unordered case. We experimentally validate the usefulness of our metrics on shape matching tasks and compare them with (an approximation of) edit-distance.

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Algorithms Artificial Intelligence Cluster Analysis Computer Simulation Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Signal Processing, Computer-Assisted Time Factors Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Andrea Torsello, Dzena Hidović-Rowe, Marcello Pelillo. 2005. Polynomial-time metrics for attributed trees.. https://doi.org/10.1109/tpami.2005.146

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