Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs

By Amir Zamir, Mubarak Shah. 2014

224 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, background): “While global features have the beneﬁt to retrieve diverse natural scene images based on ambient information, local image features yield higher precision in retrieving structured objects such as buildings and are thus more frequently used [8,9,10,11,12,13,14,15,16].”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “Most local feature based approaches therefore focus on localization within cities, either based on photos from photo sharing websites [5, 36] or street view [2, 7, 26, 27, 43, 51, 52].”
Learned Contextual Feature Reweighting for Image Geo-Localization (2017, background): “Building image representations from local invariant features have been shown to be effective as they provide robustness to photometric and geometric changes [58, 4, 67, 31, 66].”
Cross-View Image Matching for Geo-Localization in Urban Environments (2017, methodology): “For this dataset we use the list of GPS coordinates from Google Street View Dataset [28].”
Image-Based Localization Using LSTMs for Structured Feature Correlation (2016, methodology): “Given a query image, they employ image retrieval techniques to identify the database photo most similar to the query [2,41,50,51,59].”
Accurate Geo-Registration by Ground-to-Aerial Image Matching (2014, methodology): “Our contributions are: 1) a novel viewpoint-dependent matching method that handles large viewpoint changes; 2) a fully automated geo-registration pipeline for matching ground-level photos to aerial imagery; and 3) a largescale geo-registration evaluation which consists of the most popular outdoor…”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “For this reason, some approaches additionally make use of satellite aerial imagery to enhance the geolocalization in sparsely covered regions [35,40,44,45].”
Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization (2017, background): “Over the years, various improvements have been made to handle confusing [15] and repetitive [16] structures, scale to larger scenes [14], [17], or use the original feature descriptors for retrieval [18], [19].”
CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization (2018, background): “Traditional image-based geolocalization is normally done in the context where both the query and geo-tagged reference images in the database are taken from the ground view [16, 52, 33, 45].”
Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (2015, background): “Image geo-localization is the process of determining the capturing viewpoint’s positioning w.r.t. a geographic reference [43].”