Learning and calibrating per-location classifiers for visual place recognition

Petr Gronat 1 Guillaume Obozinski 2, 3 Josef Sivic 2, 3, 1 Tomas Pajdla 4
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
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 per-formed 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.
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Communication dans un congrès
CVPR 2013 - 26th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. 2013
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Petr Gronat, Guillaume Obozinski, Josef Sivic, Tomas Pajdla. Learning and calibrating per-location classifiers for visual place recognition. CVPR 2013 - 26th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. 2013. 〈hal-00934332〉

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