Next Article in Journal
Life Cycle Assessment of Lighting Systems and Light Loss Factor: A Case Study for Indoor Workplaces in France
Previous Article in Journal
Mobile P2P-Based Skyline Query Processing over Delay-Tolerant Networks
Open AccessArticle

A Multi-Column Deep Framework for Recognizing Artistic Media

by Heekyung Yang 1 and Kyungha Min 2,*
1
Industry-Academy Collaboration Foundation, Sangmyung University, Seoul 03016, Korea
2
Department of Computer Science, Sangmyung University, Seoul 0306, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1277; https://doi.org/10.3390/electronics8111277
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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop