Scene Retrieval For Contextual Visual Mapping

Abstract

Visual navigation localizes a query place image against a reference database of place images, also known as a `visual map'. Localization accuracy requirements for specific areas of the visual map, `scene classes', vary according to the context of the environment and task. State-of-the-art visual mapping is unable to reflect these requirements by explicitly targetting scene classes for inclusion in the map. Four different scene classes, including pedestrian crossings and stations, are identified in each of the Nordland and St. Lucia datasets. Instead of re-training separate scene classifiers which struggle with these overlapping scene classes we make our first contribution: defining the problem of `scene retrieval'. Scene retrieval extends image retrieval to classification of scenes defined at test time by associating a single query image to reference images of scene classes. Our second contribution is a triplet-trained convolutional neural network (CNN) to address this problem which in

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysmith2021scene

Related papers

• Visible Structure Retrieval For Lightweight Image-based Relocalisation (2025)0.00
• Investigating The Role Of Image Retrieval For Visual Localization -- An Exhaustive Benchmark (2022)16.58
• Benchmarking Image Retrieval For Visual Localization (2020)17.78
• Mapping, Localization And Path Planning For Image-based Navigation Using Visual Features And Map (2018)11.93
• Stacmr: Scene-text Aware Cross-modal Retrieval (2020)10.48
• Night-to-day Image Translation For Retrieval-based Localization (2018)16.88
• Are Local Features All You Need For Cross-domain Visual Place Recognition? (2023)13.80
• Improving Nighttime Retrieval-based Localization (2018)0.00