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Conference Papers Year : 2013

Learning and calibrating per-location classifiers for visual place recognition

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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Dates and versions

hal-00934332 , version 1 (21-01-2014)

Identifiers

  • HAL Id : hal-00934332 , version 1

Cite

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. ⟨hal-00934332⟩
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