3D head model classification by evolutionary optimization of the Extended Gaussian Image representation

Authors:
Hau-San Wong;Kent K. T. Cheung;Horace H. S. Ip
Affiliations:
Department of Computer Science, Virtual Reality Laboratory, Image Computing Group, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong;Department of Computer Science, Virtual Reality Laboratory, Image Computing Group, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong;Department of Computer Science, Virtual Reality Laboratory, Image Computing Group, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong and Centre for Innovative Applications of Inter ...
Venue:
Pattern Recognition
Year:
2004

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Abstract

Classification of 3D head models based on their shape attributes for subsequent indexing and retrieval are important in many applications, as in hierarchical content-based retrieval of these head models for virtual scene composition, and the automatic annotation of these characters in such scenes. While simple feature representations are preferred for more efficient classification operations, these features may not be adequate for distinguishing between 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 3D models which summarizes the surface normal orientation statistics, we consider these orientations as random variables, and proceed to search for an optimal transformation for these variables based on genetic optimization. The resulting transformed distributions for these random variables are 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.