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Color Constancy Convolutional Autoencoder

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Color Constancy Convolutional Autoencoder. / Laakom, Firas; Raitoharju, Jenni; Iosifidis, Alexandros; Nikkanen, Jarno; Gabbouj, Moncef.

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019. IEEE, 2019. p. 1085-1090 9002684.

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

Harvard

Laakom, F, Raitoharju, J, Iosifidis, A, Nikkanen, J & Gabbouj, M 2019, Color Constancy Convolutional Autoencoder. in 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019., 9002684, IEEE, pp. 1085-1090, IEEE Symposium Series on Computational Intelligence, 1/01/00. https://doi.org/10.1109/SSCI44817.2019.9002684

APA

Laakom, F., Raitoharju, J., Iosifidis, A., Nikkanen, J., & Gabbouj, M. (2019). Color Constancy Convolutional Autoencoder. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1085-1090). [9002684] IEEE. https://doi.org/10.1109/SSCI44817.2019.9002684

Vancouver

Laakom F, Raitoharju J, Iosifidis A, Nikkanen J, Gabbouj M. Color Constancy Convolutional Autoencoder. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019. IEEE. 2019. p. 1085-1090. 9002684 https://doi.org/10.1109/SSCI44817.2019.9002684

Author

Laakom, Firas ; Raitoharju, Jenni ; Iosifidis, Alexandros ; Nikkanen, Jarno ; Gabbouj, Moncef. / Color Constancy Convolutional Autoencoder. 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019. IEEE, 2019. pp. 1085-1090

Bibtex - Download

@inproceedings{014286315c92497a993cfc825e1263ae,
title = "Color Constancy Convolutional Autoencoder",
abstract = "In this paper, we study the importance of pretraining for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.",
keywords = "color constancy, convolutional autoencoders, illumination, pre-training",
author = "Firas Laakom and Jenni Raitoharju and Alexandros Iosifidis and Jarno Nikkanen and Moncef Gabbouj",
note = "EXT={"}Iosifidis, Alexandros{"}",
year = "2019",
doi = "10.1109/SSCI44817.2019.9002684",
language = "English",
isbn = "978-1-7281-2486-5",
pages = "1085--1090",
booktitle = "2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Color Constancy Convolutional Autoencoder

AU - Laakom, Firas

AU - Raitoharju, Jenni

AU - Iosifidis, Alexandros

AU - Nikkanen, Jarno

AU - Gabbouj, Moncef

N1 - EXT="Iosifidis, Alexandros"

PY - 2019

Y1 - 2019

N2 - In this paper, we study the importance of pretraining for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.

AB - In this paper, we study the importance of pretraining for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.

KW - color constancy

KW - convolutional autoencoders

KW - illumination

KW - pre-training

U2 - 10.1109/SSCI44817.2019.9002684

DO - 10.1109/SSCI44817.2019.9002684

M3 - Conference contribution

SN - 978-1-7281-2486-5

SP - 1085

EP - 1090

BT - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

PB - IEEE

ER -