Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Petr Gronat, Guillaume Obozinski, Josef Sivic, Tomas Pajdla; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 907-914

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.

Related Material


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[bibtex]
@InProceedings{Gronat_2013_CVPR,
author = {Gronat, Petr and Obozinski, Guillaume and Sivic, Josef and Pajdla, Tomas},
title = {Learning and Calibrating Per-Location Classifiers for Visual Place Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}