# Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

arXiv: https://arxiv.org/abs/1903.03305 · HTML: https://mlsanity.com/paper/1903.03305/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 30 of 38)

- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [AdaFusion: Visual-LiDAR Fusion with Adaptive Weights for Place Recognition](https://mlsanity.com/paper/2111.11739/) (2021)
- [SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2404.00469/) (2024)
- [Probabilistic Visual Place Recognition for Hierarchical Localization](https://mlsanity.com/paper/2105.03091/) (2021)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [Improving Visual Place Recognition Performance by Maximising Complementarity](https://mlsanity.com/paper/2102.08416/) (2021)
- [STA-VPR: Spatio-temporal Alignment for Visual Place Recognition](https://mlsanity.com/paper/2103.13580/) (2021)
- [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments](https://mlsanity.com/paper/2109.06452/) (2021)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2109.10986/) (2021)
- [Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework](https://mlsanity.com/paper/2408.11312/) (2024)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2203.00591/) (2022)
- [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness](https://mlsanity.com/paper/2107.07707/) (2021)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)
- [Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique](https://mlsanity.com/paper/2210.07509/) (2022)
- [Breaking the Frame: Visual Place Recognition by Overlap Prediction](https://mlsanity.com/paper/2406.16204/) (2024)
- [Dynamically Modulating Visual Place Recognition Sequence Length For Minimum Acceptable Performance Scenarios](https://mlsanity.com/paper/2407.00863/) (2024)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [EchoVPR: Echo State Networks for Visual Place Recognition](https://mlsanity.com/paper/2110.05572/) (2021)
- [A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments](https://mlsanity.com/paper/2303.14247/) (2023)
- [CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression](https://mlsanity.com/paper/2304.07426/) (2023)

## Abstract (arXiv)

Typical attempts to improve the capability of visual place recognition techniques include the use of multi-sensor fusion and integration of information over time from image sequences. These approaches can improve performance but have disadvantages including the need for multiple physical sensors and calibration processes, both for multiple sensors and for tuning the image matching sequence length. In this paper we address these shortcomings with a novel "multi-sensor" fusion approach applied to multiple image processing methods for a single visual image stream, combined with a dynamic sequence matching length technique and an automatic processing method weighting scheme. In contrast to conventional single method approaches, our approach reduces the performance requirements of a single image processing methodology, instead requiring that within the suite of image processing methods, at least one performs well in any particular environment. In comparison to static sequence length techniques, the dynamic sequence matching technique enables reduced localization latencies through analysis of recognition quality metrics when re-entering familiar locations. We evaluate our approach on multiple challenging benchmark datasets, achieving superior performance to two state-of-the-art visual place recognition systems across environmental changes including winter to summer, afternoon to morning and night to day. Across the four benchmark datasets our proposed approach achieves an average F1 score of 0.96, compared to 0.78 for NetVLAD and 0.49 for SeqSLAM. We provide source code for the multi-fusion method and present analysis explaining how superior performance is achieved despite the multiple, disparate, image processing methods all being applied to a single source of imagery, rather than to multiple separate sensors.