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Golden jackal optimization with lateral inhibition for image matching

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Abstract

Image matching is a consequential branch of the discipline that integrates several theories and technologies, such as pattern recognition, image processing, computer vision and feature extraction. This investigation constructs a golden jackal optimization (GJO) established on lateral inhibition (LI) named LI-GJO to accomplish this issue. The stated intention is to execute a pixel-for-pixel comparison methodology to locate the appropriate portion of the template photograph in the original photograph and guarantee the matching precision by assessing the similarities or distinctions between the two photographs. The GJO, motivated by the jackal’s synchronized foraging, exhibits its procedure of scavenging for prey, wrap** around prey and attacking prey to furnish the most appropriate solution. The GJO manipulates discovery and extraction to arrive at exceptional measured precision and prompt convergence productivity. The lateral inhibition preprocesses the original and template photographs to compensate for the visual information’s loss, strengthen the gray gradient and spatial resolution, and upgrade the photograph matching precision. To ascertain the sustainability and adaptability, the LI-GJO has been contrasted with LI-AO, LI-DOA, LI-SHO, LI-SMA, LI-SOA, LI-STOA and LI-TSA. The experimental conclusions exhibit that the LI-GJO has substantial consistency and durability to prohibit precocious convergence and attain a higher anticipated precision, greater matching proficiency, better convergence effectiveness and stronger stability. The LI-GJO is an appropriate and practical methodology for addressing image matching.

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Acknowledgements

This work was partially funded by Start-up Fund for Distinguished Scholars of West Anhui University under Grant Nos. WGKQ2022006, WGKQ2022050 and WGKQ2022052, the Scientific Research Projects of Universities in Anhui Province under Grant Nos. 2022AH051674 and 2022AH040241, the University Synergy Innovation Program of Anhui Province under Grant No. GXXT-2021-026, Smart Agriculture and Forestry and Smart Equipment Scientific Research and Innovation Team (Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center) under Grant No. 2022AH010091, School-level quality engineering (school-enterprise cooperation development curriculum resource construction) under Grant No. wxxy2022101. The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. School of Electrical and Optoelectronic Engineering, West Anhui University, Lu’an, 237012, China

    **zhong Zhang, Gang Zhang, Min Kong, Tan Zhang & Duansong Wang

Authors
  1. **zhong Zhang
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  2. Gang Zhang
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  3. Min Kong
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  4. Tan Zhang
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Contributions

**zhong Zhang: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing – original draft. Gang Zhang: Conceptualization, Methodology, Resources, Project administration, Funding acquisition. Min Kong: Conceptualization, Methodology, Writing – review & editing, investigation. Tan Zhang: Validation, Writing – review & editing. Duansong Wang: Project administration, Conceptualization, Supervision.

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Correspondence to Gang Zhang.

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Zhang, J., Zhang, G., Kong, M. et al. Golden jackal optimization with lateral inhibition for image matching. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18994-3

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