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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CF8B-A
Abstract
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.