Accession Number:
AD1039705
Title:
Learning and Calibrating Per Location Classifiers for Visual Place Recognition (Open Access)
Descriptive Note:
Conference Paper
Corporate Author:
INRIA Le Chesnay France
Personal Author(s):
Report Date:
2013-06-23
Pagination or Media Count:
8.0
Abstract:
The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database. This is a challenging task due to changes in viewpoint, imaging conditions and the large size of the image database. The contribution of this work is two-fold. First, we cast the place recognition problem as a classification task and use the available geotags to train a classifier for each location in the database in a similar manner to per-exemplar SVMs in object recognition. Second, as only few positive training examples are available for each location, we propose a new approach to calibrate all the per-location SVM classifiers using only the negative examples. The calibration we propose relies on a significance measure essentially equivalent to the p-values classically used in statistical hypothesis testing. Experiments are performed on a database of 25,000 geotagged street view images of Pittsburgh and demonstrate improved place recognition accuracy of the proposed approach over the previous work.
Descriptors:
Subject Categories:
- Cybernetics