Cross-view Image Retrieval -- Ground To Aerial Image Retrieval Through Deep Learning
2020 · Numan Khurshid, Talha Hanif, Mohbat Tharani, et al.
Abstract
Cross-modal retrieval aims to measure the content similarity between different types of data. The idea has been previously applied to visual, text, and speech data. In this paper, we present a novel cross-modal retrieval method specifically for multi-view images, called Cross-view Image Retrieval CVIR. Our approach aims to find a feature space as well as an embedding space in which samples from street-view images are compared directly to satellite-view images (and vice-versa). For this comparison, a novel deep metric learning based solution "DeepCVIR" has been proposed. Previous cross-view image datasets are deficient in that they (1) lack class information; (2) were originally collected for cross-view image geolocalization task with coupled images; (3) do not include any images from off-street locations. To train, compare, and evaluate the performance of cross-view image retrieval, we present a new 6 class cross-view image dataset termed as CrossViewRet which comprises of images inclu
Authors
(none)
Tags
Stats
Related papers
- CMIR-NET : A Deep Learning Based Model For Cross-modal Retrieval In Remote Sensing (2019)13.34
- C-BEV: Contrastive Bird's Eye View Training For Cross-view Image Retrieval And 3-dof Pose Estimation (2023)0.00
- From Street To Orbit: Training-free Cross-view Retrieval Via Location Semantics And LLM Guidance (2025)0.00
- Retrieval-guided Cross-view Image Synthesis (2024)0.00
- Cross-view Image Matching For Geo-localization In Urban Environments (2017)17.16
- BEV-CV: Birds-eye-view Transform For Cross-view Geo-localisation (2023)5.84
- VIGOR: Cross-view Image Geo-localization Beyond One-to-one Retrieval (2020)21.49
- Cross-modality Sub-image Retrieval Using Contrastive Multimodal Image Representations (2022)6.32