A Hybrid Rao-NM Algorithm for Image Template Matching

Xinran Liu; Zhongju Wang; Long Wang; Chao Huang; Xiong Luo

doi:10.3390/e23060678

A Hybrid Rao-NM Algorithm for Image Template Matching

Xinran Liu, Zhongju Wang, Long Wang^*, Chao Huang, Xiong Luo

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

8 Citations (Scopus)

83 Downloads (CityUHK Scholars)

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.

Original language	English
Article number	678
Journal	Entropy
Volume	23
Issue number	6
Online published	27 May 2021
DOIs	https://doi.org/10.3390/e23060678
Publication status	Published - Jun 2021
Externally published	Yes

Funding

This work was supported in part by the National Key R&D Program of China under Grant 2018YFC0810601, in part by in part by the National Natural Science Foundation of China under Grants 62002016 and U1836106, in part by the Guangdong Basic and Applied Basic Research Foundation under Grants 2020A1515110431 and 2019A1515111165, in part by Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB under Grants BK19BF006 and BK20BF010, in part by the Interdisciplinary Research Project for Young Teachers of USTB (Funda-mental Research Funds for the Central Universities) under Grant FRF‐IDRY‐19‐017, and in part by the Fundamental Research Funds for the Central Universities under Grants 06500078 and 06500103.

Research Keywords

Computational intelligence
Image matching
Optimization
Rao algorithm

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

Access Output

10.3390/e23060678

123522434.pdfFinal Published version, 5.6 MBLicence: CC BY

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{977e5aa7ebc54dc698ff5b0db68754b1,

title = "A Hybrid Rao-NM Algorithm for Image Template Matching",

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.",

keywords = "Computational intelligence, Image matching, Optimization, Rao algorithm",

author = "Xinran Liu and Zhongju Wang and Long Wang and Chao Huang and Xiong Luo",

note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = jun,

doi = "10.3390/e23060678",

language = "English",

volume = "23",

journal = "Entropy",

issn = "1099-4300",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "6",

}

TY - JOUR

T1 - A Hybrid Rao-NM Algorithm for Image Template Matching

AU - Liu, Xinran

AU - Wang, Zhongju

AU - Wang, Long

AU - Huang, Chao

AU - Luo, Xiong

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

KW - Computational intelligence

KW - Image matching

KW - Optimization

KW - Rao algorithm

UR - http://www.scopus.com/inward/record.url?scp=85107798855&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0- 85107798855&origin=recordpage

U2 - 10.3390/e23060678

DO - 10.3390/e23060678

M3 - RGC 21 - Publication in refereed journal

C2 - 34072269

AN - SCOPUS:85107798855

SN - 1099-4300

VL - 23

JO - Entropy

JF - Entropy

IS - 6

M1 - 678

ER -

A Hybrid Rao-NM Algorithm for Image Template Matching

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this