Graph-Based Discriminative Learning for Location Recognition

By Song Cao, Noah Snavely. 2013

154 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Graph Learning

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Citations in the corpus

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, methodology): “Pose estimation approaches [9,18,19,20,21,22,23] match query images against 3D models of an area, and employ 2D-3D feature correspondences to identify 6-DOF query poses.”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, methodology): “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].”
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition (2015, background): “Visual place recognition has received a significant amount of attention in the past years both in computer vision [5, 10, 11, 24, 35, 62, 63, 64, 65, 79, 80] and robotics communities [16, 17, 44, 46, 74] motivated by, e.”
24/7 Place Recognition by View Synthesis (2018, unknown): “RECENT years have seen a tremendous progress [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] in the large-scale visual place recognition problem [14], [17].”
Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints (2016, methodology): “Unlike other methods that use the nearest neighbor graphs to learn similarity functions [13,14], we use the graph to mine constraints for learning rich visual representations.”
Learned Contextual Feature Reweighting for Image Geo-Localization (2017, background): “There have been attempts that try to address this problem by casting feature selection as a classification problem [28], reweighting features by computing their distinctiveness in feature space [4], and discarding features that appear in multiple geo-locations [29, 9, 45, 49].”
Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (2015, background): “Some cast the localization problem as a classification problem where visual words are weighed according to their importance to specific locations [4, 12].”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, background): “ the perspective-n-point (PnP) problem [38]. Since the descriptor matching problem becomes computationally expensive when matching against very large 3D models, hybrid methods have been proposed that [39,20,19] ﬁrst perform ecient image retrieval using inverted ﬁles and then solve the PnP problem ba”
Dataset fingerprints: Exploring image collections through data mining (2015, methodology): “A graph based location recognition method is presented in [1] where nodes correspond to images.”
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition (2013, background): “2012); (ii) constructing an image graph (Cao and Snavely 2013; Philbin et al. 2010; Turcot and Lowe 2009), where images are nodes and edges connect close-by images on the map (Torii et al.”
Visual Camera Re-Localization From RGB and RGB-D Images Using DSAC (2020, background): “The metric to compare global descriptors can be learned as well [23].”
Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map (2017, background): “, [6, 8, 11, 19, 25, 28]) try to identify the similar databased images depicting the same landmarks as the query image.”
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions (2018, background): “They learn features with stable appearance over time [17, 47, 49], train classifiers for place recognition [12, 28, 37, 76], and train CNNs to regress 2D-3D matches [8, 9, 60] or camera poses [19, 31, 74].”
Unsupervised Non-parametric Geospatial Modeling from Ground Imagery (2014, background): “match locations based on visual features of specific landmarks [3].”
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression (2019, methodology): “Image retrieval has also been used as part of structure-based approaches [14,26,57].”
Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition (2020, methodology): “comparison, the classification-based approaches [22], [23] train classifiers for each location using the feature representa-”
Image-Based Localization Using LSTMs for Structured Feature Correlation (2016, background): “and Cao & Snavely learn linear classifiers on top of a standard bagof-words representation [7, 17].”