# A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition Source: [A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:26:56.232Z Authors: Nikhil Varma Keetha, Michael Milford, Sourav Garg Published: 2021-07-06T07:38:47.000Z ## Abstract Visual Place Recognition (VPR) approaches have typically attempted to match places by identifying visual cues, image regions or landmarks that have high \`\`utility'' in identifying a specific place. But this concept of utility is not singular - rather it can take a range of forms. In this paper, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues \`specific' to an environment, and to a particular place. We employ contrastive learning principles to estimate both the environment- and place-specific utility of Vector of Locally Aggregated Descriptors (VLAD) clusters in an unsupervised manner, which is then used to guide local feature matching through keypoint selection. By combining these two utility measures, our approach achieves state-of-the-art performance on three challenging benchmark datasets, while simultaneously reducing the required storage and compute time. We provide further analysis demonstrating that unsupervised cluster selection results in semantically meaningful results, that finer grained categorization often has higher utility for VPR than high level semantic categorization (e.g. building, road), and characterise how these two utility measures vary across different places and environments. Source code is made publicly available at https://github.com/Nik-V9/HEAPUtil. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Visual Place Recognition on Berlin Kudamm](): Visual Place Recognition; Berlin Kudamm; Recall@1: 61.07. - [Visual Place Recognition on Nordland Summer vs Winter](): Visual Place Recognition; Nordland Summer vs Winter; Recall@1: 29.34. - [Visual Place Recognition on Oxford RobotCar Day vs Night](): Visual Place Recognition; Oxford RobotCar Day vs Night; Recall@1: 86.06. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()