Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5879
Title: Painter Prediction from Artworks with Transfer Learning
Authors: Cömert, Ceren
Özbayoğlu, M.
Kasnakoğlu, Coşku
Keywords: artists classification
convolutional neural network
transfer learning
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021, 3 February 2021 through 5 February 2021, , 168272
Abstract: This paper provides information about prediction of our national painters from their paintings. Since most of the painters whose paintings are used in this research lived in the past, many of their artwork couldn't reach the present and couldn't be found on one web site on the internet. Moreover, some of them have paintings less than a hundred. As result, an original dataset is created from these painters paintings, which are collected at different sources. Since the art classification is a difficult task and takes many years to expertise, using machine learning techniques can provide efficiency. Purpose of this paper is to identify and classify national artists from their artwork using machine learning techniques. In order to solve art classification problem, transfer learning with four different architectures is used and the results are compared. © 2021 IEEE.
URI: https://doi.org/10.1109/ICMRE51691.2021.9384828
https://hdl.handle.net/20.500.11851/5879
ISBN: 9780738132051
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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