From images to scenes: Compressing an image cluster into a single scene model for place recognition

By Edward Johns, Guang-Zhong Yang. 2011

36 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, 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): “Instead of matching against a ﬂat collection of photos, landmark recognition systems [1, 14, 22, 37, 54] build a database of landmark buildings by clustering images from internet photo collections.”
Pairwise Decomposition of Image Sequences for Active Multi-view Recognition (2016, background): “…can be found by tracking those which are shared across multiple views and clustering images accordingly [23], or by learning their relative 2D displacements as the viewpoint changes, both with hard constraints for rigid bodies [17, 18] and ﬂexible constraints for deformable bodies [11, 10].”
Graph-Based Discriminative Learning for Location Recognition (2013, methodology): “Some methods use iconic images to represent sets of images taken from very similar viewpoints [15, 13].”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “, they perform image retrieval based on both Gist features and BoVWs and apply a threshold to the retrieval score to decide if the query contains the object. Both Avrithis et al. [1] and Johns et al. [26] compress the images in a cluster into a joint BoVW representation and perform inverted ﬁle retr”