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Deep models of painting authorship

Nejc Ilenič (2017) Deep models of painting authorship. MSc thesis.

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    An increasing number of studies are investigating how to automatically recognize painters from digital artwork images. We approach this problem in a supervised manner, by training a high-capacity convolutional neural network, capable of predicting a large number of artists from low-resolution scans. We evaluate the proposed solution in a Kaggle competition, in which pairs of paintings need to be classified based on the identity of their authors. The main contribution of our work is the provision of empirical evidence that themes and motifs, similar to low-level features, contain discriminative potential for identifying painters.

    Item Type: Thesis (MSc thesis)
    Keywords: machine learning, deep models, convolutional neural networks, artwork images
    Number of Pages: 62
    Language of Content: English
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Blaž Zupan106Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537581763)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3976
    Date Deposited: 22 Sep 2017 14:38
    Last Modified: 10 Oct 2017 12:51
    URI: http://eprints.fri.uni-lj.si/id/eprint/3976

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