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Deep multiresolution color constancy

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE COMPUTER SOCIETY PRESS
Pages3735-3739
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 20 Feb 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Abstract

In this paper, a computational color constancy method is proposed via estimating the illuminant chromaticity in a scene by pooling from many local estimates. To this end, first, for each image in a dataset, we form an image pyramid consisting of several scales of the original image. Next, local patches of certain size are extracted from each scale in this image pyramid. Then, a convolutional neural network is trained to estimate the illuminant chromaticity per-patch. Finally, two more consecutive trainings are conducted, where the estimation is made per-image via taking the mean (1st training) and median (2nd training) of local estimates. The proposed method is shown to outperform the state-of-the-art in a widely used color constancy dataset.

Keywords

  • Color constancy, Deep learning, Illuminant chromaticity estimation, Local estimation, Multi-resolution

Publication forum classification

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