Visual localization by linear combination of image descriptors

We seek to predict the GPS location of a query image given a database of images localized on a map with known GPS locations. The contributions of this work are three-fold: (1) we formulate the image-based localization problem as a regression on an image graph with images as nodes and edges connecting close-by images; (2) we design a novel image matching procedure, which computes similarity between the query and pairs of database images using edges of the graph and considering linear combinations of their feature vectors. This improves generalization to unseen viewpoints and illumination conditions, while reducing the database size; (3) we demonstrate that the query location can be predicted by interpolating locations of matched images in the graph without the costly estimation of multi-view geometry. We demonstrate benefits of the proposed image matching scheme on the standard Oxford building benchmark, and show localization results on a database of 8,999 panoramic Google Street View images of Pittsburgh.