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Using flower pollination algorithm and atomic potential function for shape matching

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Abstract

Visual shape matching has been a hot research topic. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost-effective in the computation process. The optimization process of shape matching can be regarded as a numerical optimization problem, which is disposed by flower pollination algorithm (FPA). This study comprehensively investigates the convergence performances of FPA and the other algorithms in shape matching problem based on APM model. Experimental results of three realistic examples show that FPA is able to provide very competitive results and to outperform the other algorithms.

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Acknowledgments

The authors would like to thank the anonymous reviewers whose valuable comments have greatly improved the presentation of the paper. This work is supported by National Science Foundation of China under Grants Nos. 61463007, 6153008.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Yongquan Zhou, Sen Zhang, Qifang Luo & Chunming Wen

  2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Yongquan Zhou & Qifang Luo

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  1. Yongquan Zhou
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  2. Sen Zhang
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  3. Qifang Luo
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  4. Chunming Wen
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Correspondence to Yongquan Zhou.

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Zhou, Y., Zhang, S., Luo, Q. et al. Using flower pollination algorithm and atomic potential function for shape matching. Neural Comput & Applic 29, 21–40 (2018). https://doi.org/10.1007/s00521-016-2524-0

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  • DOI: https://doi.org/10.1007/s00521-016-2524-0

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