Polynomial-time metrics for attributed trees
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 r...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 27(2005), 7 vom: 01. Juli, Seite 1087-99 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2005
|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | 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 |
|---|---|
| Beschreibung: | Date Completed 11.08.2005 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1939-3539 |