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- W2769172727 abstract "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." @default.
- W2769172727 created "2017-12-04" @default.
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- W2769172727 date "2013-06-23" @default.
- W2769172727 modified "2023-09-27" @default.
- W2769172727 title "Learning and Calibrating Per Location Classifiers for Visual Place Recognition (Open Access)" @default.
- W2769172727 hasPublicationYear "2013" @default.
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