Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  3DString: a feature string kernel for 3D object classification on voxelized data

Assfalg, J., Borgwardt, K., & Kriegel, H.-P. (2006). 3DString: a feature string kernel for 3D object classification on voxelized data. In 15th ACM International Conference on Information and Knowledge Management (CIKM 2006) (pp. 198-207). New York, NY, USA: ACM Press.

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Assfalg, J, Autor
Borgwardt, KM1, Autor           
Kriegel, H-P, Autor
Yu, Herausgeber
P.S., Herausgeber
Tsotras, V.J., Herausgeber
Fox, E.A., Herausgeber
Liu, B., Herausgeber
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Classification of 3D objects remains an important task in many areas of data management such as engineering, medicine or biology. As a common preprocessing step in current approaches to classification of voxelized 3D objects, voxel representations are transformed into a feature vector description.In this article, we introduce an approach of transforming 3D objects into feature strings which represent the distribution of voxels over the voxel grid. Attractively, this feature string extraction can be performed in linear runtime with respect to the number of voxels. We define a similarity measure on these feature strings that counts common k-mers in two input strings, which is referred to as the spectrum kernel in the field of kernel methods. We prove that on our feature strings, this similarity measure can be computed in time linear to the number of different characters in these strings. This linear runtime behavior makes our kernel attractive even for large datasets that occur in many application domains. Furthermore, we explain that our similarity measure induces a metric which allows to combine it with an M-tree for handling of large volumes of data. Classification experiments on two published benchmark datasets show that our novel approach is competitive with the best state-of-the-art methods for 3D object classification.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2006-11
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISBN: 1-59593-433-2
URI: http://www.informatik.uni-trier.de/~ley/db/conf/cikm/cikm2006.html
DOI: 10.1145/1183614.1183647
BibTex Citekey: AssfalgBK2006
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 15th ACM International Conference on Information and Knowledge Management (CIKM 2006)
Veranstaltungsort: Arlington, VA, USA
Start-/Enddatum: -

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: 15th ACM International Conference on Information and Knowledge Management (CIKM 2006)
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: New York, NY, USA : ACM Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 198 - 207 Identifikator: -