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Application of evolutionary strategies for 3D graphical model categorization and retrieval

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Abstract

In multimedia information processing, while the previous focus was on image/video retrieval, content-based categorization and retrieval of 3D computer graphics model is becoming increasingly important. This is due to the increased adoption of 3D graphics representations in multimedia applications and the resulting need for rapid virtual scene assembly from a repository of 3D models. Motivated by these requirements, the main focus of this paper is on the content-based classification and retrieval of 3D computer graphics models based on a histogram feature representation, and the search for an adaptive transformation of this representation such that the resulting classification and retrieval accuracies are optimized. Observing that a histogram is basically an approximation of the probability density function of an underlying random variable, and that a suitable transformation, when applied to the random variable, will allow the classifier to attain better accuracy based on this new representation, we propose an evolutionary optimization approach to search for this set of optimal transformations due to the large size of the search space. In particular, we consider the special class of transformations that take the form of a piecewise continuous mapping. In this case, the transformed variable is a mixed random variable, with both discrete and continuous components, which provides added flexibility for modeling a number of more diverse random variable types. With a suitably defined fitness function for evolutionary strategies (ES) that measures the capability of the transformed histogram representation to induce the correct class structure, our proposed approach is capable of improving the head model classification performance, which in turn allows, in the case of content-based retrieval, the correct preassignment of a query object to its correct class for more efficient search, even in those cases where the query is ambiguous and difficult to characterize.

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Authors and Affiliations

  1. Image Computing Group, Department of Computer Science, City University of Hong Kong, Hong Kong

    Hau-San Wong, Kent K. T. Cheung & Chun-Ip Chiu

  2. Image Computing Group, Department of Computer Science, City University of Hong Kong, Hong Kong

    Horace H. S. Ip

  3. Centre for Innovative Applications of Internet and Multimedia Technologies (AIMtech Center), City University of Hong Kong, Hong Kong

    Horace H. S. Ip

Authors
  1. Hau-San Wong
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  2. Kent K. T. Cheung
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  3. Chun-Ip Chiu
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  4. Horace H. S. Ip
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Corresponding author

Correspondence to Hau-San Wong.

Additional information

Hau San Wong is currently an assistant professor in the Department of Computer Science, City University of Hong Kong. He received the B.Sc. and M.Phil. degrees in electronic engineering from the Chinese University of Hong Kong and the Ph.D. degree in electrical and information engineering from the University of Sydney. He has also held research positions at the University of Sydney and Hong Kong Baptist University. His research interests include multimedia signal processing, neural networks, and evolutionary computation. He is the coauthor of the book Adaptive Image Processing: A Computational Intelligence Perspective, which is a joint publication of CRC Press and SPIE Press, and was an organizing committee member of the 2000 IEEE Pacific Rim Conference on Multimedia and 2000 IEEE Workshop on Neural Networks for Signal Processing, both of which were held in Sydney, Australia. He has also co-organized a number of conference special sessions, including the special session on “Image Content Extraction and Description for Multimedia” at the 2000 IEEE International Conference on Image Processing, Vancouver, Canada, and “Machine Learning Techniques for Visual Information Retrieval” at the 2003 International Conference on Visual Information Retrieval, Miami, FL.

K.T. Cheung received his B.Sc. (first class honours) and Ph.D. in the Department of Computer Science, City University of Hong Kong in 1996 and 2002, respectively. He worked as a research staff and a part-time lecturer in the same department until 2004. During his years at City University of Hong Kong, he was involved in a wide range of projects, such as content-based retrieval of color logos, intelligent retrieval of histological images, 3D head model classification and retrieval, and an object-oriented framework for image representation and retrieval. In 2004 he joined the Department of Computing at the Hong Kong Polytechnic University as a visiting assistant professor. His research interests include content-based retrieval of images and 3D models, image and 3D model classification, and evolutionary optimization.

Chun Ip Chiu received his B.S. with first class honors in 2002 and his M.Phil. in computer science in 2004, both from the City University of Hong Kong. His research interests include image processing, evolutionary computation, and content-based image retrieval and classification.

Horace H. S. Ip received his B.Sc. (first class honors) in applied physics and Ph.D. in image processing from University College London, UK in 1980 and 1983, respectively. Presently, he is the chair professor of the computer science department and the founding director of the AIMtech Centre (Centre for Innovative Applications of Internet and Multimedia Technologies) at the City University of Hong Kong. His research interests include image processing and analysis, pattern recognition, hypermedia systems in education, and computer graphics. Prof. Ip is the chairman of the IEEE (Hong Kong section) Computer Chapter and the founding president of the Hong Kong Society for Multimedia and Image Computing. He has published over 160 papers in international journals and conference proceedings. Prof. Ip is a member of the IEEE, a fellow of the Hong Kong Institution of Engineers (HKIE), fellow of the Institution of Engineers (IEE), UK, and fellow of the International Association for Pattern Recognition (IAPR).

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Wong, HS., Cheung, K.K.T., Chiu, CI. et al. Application of evolutionary strategies for 3D graphical model categorization and retrieval. Multimedia Systems 10, 422–431 (2005). https://doi.org/10.1007/s00530-005-0171-x

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