Retrieving landmark and non-landmark images from community photo collections

By Yannis Avrithis, Yannis Kalantidis, Giorgos Tolias, Evaggelos Spyrou. 2010

120 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Retrieval & Search

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Citations in the corpus

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, methodology): “The database serves as the index of an image retrieval sys-tem [28,29,30,31,32,33] or the training data of a landmark classiﬁer [34,35,36].”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “5 shows examples of the different types of images PlaNet can localize. landmarks, which can also be recognized by landmark recognition engines [1, 37, 54], PlaNet can often correctly localize street scenes, landscapes, buildings of characteristic architecture, locally typical objects like red phone…”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “Landmark object discovery approaches [1, 11, 4, 5, 6] aim at finding interesting buildings and other objects, but no systematic evaluation has been performed that analyzes what types of objects can be discovered and how recognition performance varies with these types.”
The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos (2014, background): “A few related work attempt to recognize the place name given a geo-tagged image[22, 12, 4, 5], with the objective similar to ours.”
Image Retrieval for Image-Based Localization Revisited (2012, background): “increase recall and decrease the length of the inverted files [1].”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “Approaches that focus on landmark recognition use either a pre-defined set of landmarks or cluster a given photo collection in an unsupervised manner to retrieve the most interesting areas for geolocalization [4,23,28,48].”
Improving Image-Based Localization by Active Correspondence Search (2012, background): “Recently proposed systems are able to handle hundreds of thousands [12, 13] or even millions of images [2, 9].”
Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval (2020, background): “This dataset also has a version with 1 M images from 22 cities where the annotated images come from a single city [4].”