Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212089" target="_blank" >RIV/68407700:21230/13:00212089 - isvavai.cz</a>

Result on the web

<a href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Gronat_Learning_and_Calibrating_2013_CVPR_paper.pdf" target="_blank" >http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Gronat_Learning_and_Calibrating_2013_CVPR_paper.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Original language description

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 objectrecognition. 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 significancemeasure 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 ac

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/7E13015" target="_blank" >7E13015: Planetary Robotics Data Exploitation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2013

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

CVPR 2013: IEEE Computer Society Conference on Computer Vision and Pattern Recognition

ISBN

978-0-7695-4989-7

ISSN

1063-6919

e-ISSN

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Number of pages

8

Pages from-to

907-914

Publisher name

IEEE Computer Society

Place of publication

Los Alamitos

Event location

Portland

Event date

Jun 23, 2013

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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