Graph-Based Discriminative Learning for Location Recognition

Recognizing the location of a query image by matching it to an image 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 an image database by representing it as a graph, and show how the rich information embedded in such a graph can improve bag-of-words-based location recognition methods. In particular, starting from a graph based on visual connectivity, we propose a method for selecting a set of overlapping subgraphs and learning a local distance function for each subgraph 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.