Employing Universal Voting Schemes for Improved Visual Place Recognition Performance
<|reference_start|>Employing Universal Voting Schemes for Improved Visual Place Recognition Performance: Visual Place Recognition has been the subject of many endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion, Fly-Inspired Voting Units, SwitchHit or Switch-Fuse involve combining different VPR techniques together, utilizing different strategies. However, a major aspect often common to many of these strategies is voting. Voting is an extremely relevant topic to explore in terms of its application and significance for any ensemble VPR setup. This paper analyses several voting schemes to maximise the place detection accuracy of a VPR ensemble set up and determine the optimal voting schemes for selection. We take inspiration from a variety of voting schemes that are widely employed in fields such as politics and sociology and it is evident via empirical data that the selection of the voting method influences the results drastically. The paper tests a wide variety of voting schemes to present the improvement in the VPR results for several data sets. We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment. We propose a ranking of these different voting methods from best to worst which allows for better selection. While presenting our results in terms of voting method's performance bounds, in form of radar charts, PR curves to showcase the difference in performance and a comparison methodology using a McNemar test variant to determine the statistical significance of the differences. This test is performed to further confirm the reliability of outcomes and draw comparisons for better and informed selection a voting technique.<|reference_end|>
@article{waheed2024employing,
    title={Employing Universal Voting Schemes for Improved Visual Place Recognition
  Performance},
    author={Maria Waheed, Michael Milford, Xiaojun Zhai, Maria Fasli, Klaus
  McDonald-Maier and Shoaib Ehsan},
    journal={arXiv preprint arXiv:2405.02297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02297},
    primaryClass={cs.CV}
}