A Hybrid Rao-NM Algorithm for Image Template Matching |
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| Authors: | Xinran Liu Zhongju Wang Long Wang Chao Huang Xiong Luo |
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| Institution: | 1.School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (X.L.); (Z.W.); (C.H.); (X.L.);2.Shunde Graduate School, University of Science and Technology Beijing, Foshan 528300, China;3.Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China |
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| Abstract: | This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems. |
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| Keywords: | image matching Rao algorithm computational intelligence optimization |
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