An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition
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Abstract (arXiv)
Visual Place Recognition has recently seen a surge of endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion or switching involve combining different VPR techniques together, utilizing different strategies. One major aspect often common to many of these strategies is voting. Voting is widely used in many ensemble methods, so it is potentially a relevant subject to explore in terms of its application and significance for improving VPR performance. This paper attempts to looks into detail and analyze a variety of voting schemes to evaluate which voting technique is optimal for an ensemble VPR set up. We take inspiration from a variety of voting schemes that exist and are widely employed in other research fields such as politics and sociology. The idea is inspired by an observation that different voting methods result in different outcomes for the same type of data and each voting scheme is utilized for specific cases in different academic fields. Some of these voting schemes include Condorcet voting, Broda Count and Plurality voting. Voting employed in any aspect requires that a fair system be established, that outputs the best and most favourable results which in our case would involve improving VPR performance. We evaluate some of these voting techniques in a standardized testing of different VPR techniques, using a variety of VPR 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 also aim to propose a ranking of these different voting methods from best to worst according to our results as this will allow for better selection of voting schemes.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 9 May 2023.
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References · 16
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
- 2020VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Changecited by 101
- 2017Deep Learning Features at Scale for Visual Place Recognitioncited by 96
- 2015On the Performance of ConvNet Features for Place Recognitioncited by 72
- 2018A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changescited by 53
- 2019Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methodscited by 38
- 2018LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semanticscited by 34
- 2018Lightweight Unsupervised Deep Loop Closurecited by 32
- 2019A Cross-Season Correspondence Dataset for Robust Semantic Segmentationcited by 28
- 2020Hierarchical Multi-Process Fusion for Visual Place Recognitioncited by 24
- 2018Memorable Maps: A Framework for Re-defining Places in Visual Place Recognitioncited by 16
- 2021Improving Visual Place Recognition Performance by Maximising Complementaritycited by 15
4 more
- 2021An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environmentscited by 12
- 2022SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environmentscited by 8
- 2016Visual Place Recognition with Probabilistic Vertex Votingcited by 5
- 20162D Visual Place Recognition for Domestic Service Robots at Nightcited by 3
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