An evolutionary optimization approach for 3D human head model classification

Classification of 3-D head models based on their shape attributes for subsequent indexing and retrieval are important in many applications, as in the selection and generation of human characters in virtual scenes, and the composition of morphing sequences requiring a qualitatively similar target head model. Simple feature representations are more efficient but may not be adequate for distinguishing the subtly different head model classes. In view of these, we propose an optimization approach based on genetic algorithm (GA) where the original model representation is transformed in such a way that the classification rate is significantly enhanced while retaining the efficiency and simplicity of the original representation. Specifically, based on the Extended Gaussian Image (EGI) representation for 3-D models which summarizes the surface normal orientation statistics, we consider these orientations as a random variable, and proceed to search for an optimal transformation for this variable based on genetic optimization. The resulting transformed distribution for the random variable is then used as the modified classifier inputs. Experiments have shown that the optimized transformation results in a significant improvement in classification results for a large variety of class structures. More importantly, the transformation can be indirectly realized by bin removal and bin count merging in the original histogram, thus retaining the advantage of the original EGI representation.