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Evolutionary computation based optimization of image Zernike moments shape feature vector

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Wuhan University Journal of Natural Sciences

Abstract

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Maofu Liu & Hujun Hu

  2. School of Computer, Wuhan University, Wuhan, 430072, Hubei, China

    Ming Zhong, Yanxiang He & Fazhi He

Authors
  1. Maofu Liu
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  2. Hujun Hu
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  3. Ming Zhong
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  4. Yanxiang He
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  5. Fazhi He
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Corresponding author

Correspondence to Maofu Liu.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (60303029)

Biography: LIU Maofu (1977–), male, Associate professor, Ph.D., research direction: image mining, natural language processing.

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Liu, M., Hu, H., Zhong, M. et al. Evolutionary computation based optimization of image Zernike moments shape feature vector. Wuhan Univ. J. Nat. Sci. 13, 153–158 (2008). https://doi.org/10.1007/s11859-008-0206-1

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  • DOI: https://doi.org/10.1007/s11859-008-0206-1

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