Abstract
Curators, art historians, and connoisseurs are often interested in determining the authorship of paintings. Machine learning and image processing techniques can assist in this task by providing non-invasive, automatic, and objective methods. In this work, we study the automatic identification of Vincent van Gogh's paintings using a Convolutional Neural Network that extracts discriminative visual patterns of a painter directly from images, and a machine learning classifier allied with a fusion method in the final decision process. We divide each painting into non-overlapping patches, classify them individually, and then aggregate the outcomes for the final response. We find out that using the patch with highest confidence score leads to the best result, outperforming the traditional voting scheme. We also contribute with a new and public dataset for van Gogh painting identification.
Published Version
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