Empirical Inference

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

2006

Conference Paper

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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.

Author(s): Assfalg, J. and Borgwardt, KM. and Kriegel, H-P.
Pages: 198-207
Year: 2006
Month: November
Day: 0
Editors: Yu, P.S. , V.J. Tsotras, E.A. Fox, B. Liu
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1183614.1183647
Event Name: 15th ACM International Conference on Information and Knowledge Management (CIKM 2006)
Event Place: Arlington, VA, USA

Address: New York, NY, USA
Digital: 0
ISBN: 1-59593-433-2

BibTex

@inproceedings{AssfalgBK2006,
  title = {3DString: a feature string kernel for 3D object classification on voxelized data},
  author = {Assfalg, J. and Borgwardt, KM. and Kriegel, H-P.},
  pages = {198-207},
  editors = {Yu, P.S. , V.J. Tsotras, E.A. Fox, B. Liu},
  publisher = {ACM Press},
  address = {New York, NY, USA},
  month = nov,
  year = {2006},
  doi = {10.1145/1183614.1183647},
  month_numeric = {11}
}