Deep Learning Artwork Style Prediction and Similarity Detection

  1. Igor Sorarrain Rebollar 1
  2. Manuel Graña 1
  1. 1 University of the Basque Country (UPV/EHU), Donostia, Spain
Buch:
Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Alvarez Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Hojjat Adeli

Verlag: Springer Suiza

ISBN: 978-3-031-06527-9

Datum der Publikation: 2022

Seiten: 289-297

Art: Buch-Kapitel

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Zusammenfassung

The point of departure of this work is the aim to predict artwork style. The paper presents results retraining some of the most popular deep learning models for image classification, i.e. ResNet-34, ResNet-50, VGG-16, DenseNet-121, and a CNN model made from scratch over a dataset extracted from WikiArt for a Kaggle competition. This dataset is composed of 103253 images, categorized into 136 different artwork styles. We select 20 art styles that have enough image samples to allow for network training, achieving accuracy comparable to state of the art results. Moreover, we observe that the structure of the confusion matrix reflects the conceptual relations between the artwork styles, hence points to an induced similarity measure between styles of artwork instances

Fuente de los datos: Dialnet