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Title:Art painting author and style labeling using convolutional neural network
Author(s):Xu, Ke
Contributor(s):Kindratenko, Volodymyr
Subject(s):art painting
convolutional neural network
Image sampling
Image databases
Abstract:This thesis proposes a convolutional neural network-based approach for labeling art paintings by their author and style. Our Artist1000 dataset consists of 1000 raw painting images for each of 5 prolific artists. Our Style1000 dataset consists of 1000 raw painting images for each of 5 different painting styles. Two independent ResNet18 classifiers are trained and validated, one for each dataset. They are combined to predict the author and style of unseen art paintings. Our algorithm first classifies patches extracted from raw painting images and then uses majority vote of patches from the same image to predict image label. We achieve 84.3% accuracy on Artist1000 dataset and 66.9% accuracy on Style1000 dataset. Compared with the traditional methods of art painting labeling, which heavily depended on extracting complex artificial features from raw images, our algorithm shows promising results of empowering a neural network to extract local features and make predictions.
Issue Date:2018-05
Date Available in IDEALS:2018-05-25

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