Graph-Based Discriminative Learning for Location Recognition

Song Cao Noah Snavely


Recognizing the location of a query image by matching it to a database is an important problem in computer vision, and one for which the representation of the database is a key issue. We explore new ways for exploiting the structure of a database by representing it as a graph, and show how the rich information embedded in a graph can improve a bag-of-words-based location recognition method. In particular, starting from a graph on a set of images based on visual connectivity, we propose a method for selecting a set of subgraphs and learning a local distance function for each using discriminative techniques. For a query image, each database image is ranked according to these local distance functions in order to place the image in the right part of the graph. In addition, we propose a probabilistic method for increasing the diversity of these ranked database images, again based on the structure of the image graph. We demonstrate that our methods improve performance over standard bag-of-words methods on several existing location recognition datasets.

Paper - CVPR 2013

Paper - CVPR 2013 (PDF, 6.7 MB)
Poster - CVPR 2013(PDF, 9.7 MB)
Paper - IJCV 2014 (PDF, 24.6 MB)
Slides (PPTX, 3.1MB; KEY, 43.7MB)


This work was supported in part by the NSF (grants IIS-1149393 and IIS-0964027), Intel Corporation. We also thank Flickr users for use of their photos.