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%2F16%3A00304267" target="_blank" >RIV/68407700:21230/16:00304267 - isvavai.cz</a>

Result on the web

<a href="http://link.springer.com/article/10.1007/s11263-015-0878-x" target="_blank" >http://link.springer.com/article/10.1007/s11263-015-0878-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11263-015-0878-x" target="_blank" >10.1007/s11263-015-0878-x</a>

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 object recognition. Second, as only one or a few positive training examples are available for each location, we propose two methods to calibrate all the per-location SVM classifiers without the need for additional positive training data. The first method relies on p-values from statistical hypothesis testing and uses only the available negative training data. The second method performs an affine calibration by appropriately normalizing the learnt classifier hyperplane and does not need any additional labelled training data. We test the proposed place recognitionmethod with the bag-of-visual-words and Fisher vector image representations suitable for large scale indexing. Experiments are performed on three datasets: 25,000 and 55,000 geotagged street view images of Pittsburgh, and the 24/7 Tokyo benchmark containing 76,000 images with varying illumination conditions. The results show improved place recognition accuracy of the learnt image representation over direct matching of raw image descriptors.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

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)

Publication year

2016

Confidentiality

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

Name of the periodical

International Journal of Computer Vision

ISSN

0920-5691

e-ISSN

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Volume of the periodical

118

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

18

Pages from-to

319-336

UT code for WoS article

000378789100003

EID of the result in the Scopus database

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