Deep Structured-Output Regression Learning for Computational Color Constancy

Yanlin Qian, Ke Chen, Joni-Kristian Kämäräinen, Jarno Nikkanen, Jiri Matas

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

    12 Citations (Scopus)

    Abstract

    The color constancy problem is addressed by structured-output regression on the values of the fully-connected layers of a convolutional neural network. The AlexNet and the VGG are considered and VGG slightly outperformed AlexNet. Best results were obtained with the first fully-connected “fc6” layer and with multi-output support vector regression. Experiments on the SFU Color Checker and Indoor Dataset benchmarks demonstrate that our method achieves competitive performance, outperforming the state of the art on the SFU indoor benchmark.
    Original languageEnglish
    Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
    PublisherIEEE
    ISBN (Electronic)978-1-5090-4847-2
    DOIs
    Publication statusPublished - 2017
    Publication typeA4 Article in a conference publication
    EventInternational Conference on Pattern Recognition -
    Duration: 1 Jan 1900 → …

    Conference

    ConferenceInternational Conference on Pattern Recognition
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1

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