Evolutionary optimization of feature representation for 3D point-based model classification

In this paper, we introduce a new approach for the classification of point-based 3D computer graphics models. We propose a new representation for 3D point cloud models based on a set of principal projection axes. The point set is then projected on to each of these axes, and a suitable summary statistics of the projected point set along each axis is calculated. The complete set of statistics is then adopted as the feature representation of the point set. Based on this representation, we need to search for the optimal set of projection axes which can best distinguish the different classes of point cloud models in the database. In general, this optimization problem is difficult due to the size of the search space. As a result, we propose to adopt Evolutionary Strategy (ES)^[3] as the optimization technique. This is in view of the capability of ES to explore many regions of the search space in parallel. Our experiment results indicate that the proposed optimized feature representation based on only the point set can attain a classification accuracy which is comparable to alternative feature representations which require the availability of the original polygonal representation. © 2006 IEEE.