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

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...

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Johannes Aßfalg, Karsten M. Borgwardt, Hans-Peter Kriegel
• Keywords: CIKM 2006, Feature Strings, Information Management, Linear Runtime, Similarity Measure