Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Learning and Calibrating Per-Location Classifiers for Visual Place Recognition

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

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

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

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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Počet stran výsledku

8

Strana od-do

907-914

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Portland

Datum konání akce

23. 6. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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