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Open AccessArticle

A Multi-Column Deep Framework for Recognizing Artistic Media

by Heekyung Yang 1 and Kyungha Min 2,*
Industry-Academy Collaboration Foundation, Sangmyung University, Seoul 03016, Korea
Department of Computer Science, Sangmyung University, Seoul 0306, Korea
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1277;
Received: 11 September 2019 / Revised: 15 October 2019 / Accepted: 30 October 2019 / Published: 2 November 2019
(This article belongs to the Section Computer Science & Engineering)
We present a multi-column structured framework for recognizing artistic media from artwork images. We design the column of our framework using a deep neural network. Our key idea is to recognize the distinctive stroke texture of an artistic medium, which plays a key role in distinguishing artistic media. Since stroke texture is in a local scale, the whole image is not proper for recognizing the texture. Therefore, we devise two ideas for our framework: Sampling patches from an input image and employing a Gram matrix to extract the texture. The patches sampled from an input artwork image are processed in the columns of our framework to make local decisions on the patch, and the local decisions from the patches are merged to make a final decision for the input artwork image. Furthermore, we employ a Gram matrix, which is known to effectively capture texture information, to improve the accuracy of recognition. Our framework is trained and tested using two real artwork image datasets: WikiSet of traditional artwork images and YMSet of contemporary artwork images. Finally, we build SynthSet, which is a collection of synthesized artwork images from many computer graphics literature, and propose a guideline for evaluating the synthesized artwork images.
Keywords: media recognition; multi-column framework; CNN; deep learning media recognition; multi-column framework; CNN; deep learning
MDPI and ACS Style

Yang, H.; Min, K. A Multi-Column Deep Framework for Recognizing Artistic Media. Electronics 2019, 8, 1277.

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