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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2018

17-08-2016 | Original Article

Using flower pollination algorithm and atomic potential function for shape matching

Authors: Yongquan Zhou, Sen Zhang, Qifang Luo, Chunming Wen

Published in: Neural Computing and Applications | Issue 6/2018

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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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Metadata
Title
Using flower pollination algorithm and atomic potential function for shape matching
Authors
Yongquan Zhou
Sen Zhang
Qifang Luo
Chunming Wen
Publication date
17-08-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 6/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2524-0

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