Graph-Based Discriminative Learning for Location Recognition

• Published in: 2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 700 - 707
• Main Authors: Song Cao, Snavely, Noah
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2013
• Subjects: discriminative learning; Image edge detection; image graph; Image matching; Lead; location recognition; Measurement; Three-dimensional displays
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2013.96

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 sub graphs 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.