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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.
Cited literature [32 references]
https://hal.inria.fr/hal-00934332
Contributor : Petr Gronat <>
Submitted on : Tuesday, January 21, 2014 - 7:44:32 PM
Last modification on : Thursday, July 1, 2021 - 5:58:07 PM
Long-term archiving on: : Tuesday, April 22, 2014 - 10:11:13 AM
Contributor : Petr Gronat <>
Submitted on : Tuesday, January 21, 2014 - 7:44:32 PM
Last modification on : Thursday, July 1, 2021 - 5:58:07 PM
Long-term archiving on: : Tuesday, April 22, 2014 - 10:11:13 AM
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cvpr13-gronat_opt.pdf
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