No Fear of the Dark: Image Retrieval Under Varying Illumination Conditions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335541" target="_blank" >RIV/68407700:21230/19:00335541 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICCV.2019.00979" target="_blank" >https://doi.org/10.1109/ICCV.2019.00979</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCV.2019.00979" target="_blank" >10.1109/ICCV.2019.00979</a>

Result language

angličtina

Original language name

No Fear of the Dark: Image Retrieval Under Varying Illumination Conditions

Original language description

Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned. Prior to extracting image descriptors by a convolutional neural network, images are photometrically normalised in order to reduce the descriptor sensitivity to illumination changes. We propose a learnable normalisation based on the U-Net architecture, which is trained on a combination of single-camera multi-exposure images and a newly constructed collection of similar views of landmarks during day and night. We experimentally show that both hand-crafted normalisation based on local histogram equalisation and the learnable normalisation outperform standard approaches in varying illumination conditions, while staying on par with the state-of-the-art methods on daylight illumination benchmarks, such as Oxford or Paris datasets.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-23165S" target="_blank" >GA19-23165S: Generalized Image Retrieval and Relation Discovery</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Article name in the collection

2019 IEEE International Conference on Computer Vision (ICCV 2019)

ISBN

978-1-7281-4803-8

ISSN

1550-5499

e-ISSN

2380-7504

Number of pages

9

Pages from-to

9695-9703

Publisher name

IEEE Computer Society Press

Place of publication

Los Alamitos

Event location

Seoul

Event date

Oct 27, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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