NDLI: DRAW: Deep Networks for Recognizing Styles of Artists Who Illustrate Children's Books

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DRAW: Deep Networks for Recognizing Styles of Artists Who Illustrate Children's Books

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DRAW: Deep Networks for Recognizing Styles of Artists Who Illustrate Children's Books

Content Provider	ACM Digital Library
Author	Sener, Fadime Hicsonmez, Samet Samet, Nermin Duygulu, Pinar
Abstract	This paper is motivated from a young boy's capability to recognize an illustrator's style in a totally different context. In the book "We are All Born Free" [1], composed of selected rights from the Universal Declaration of Human Rights interpreted by different illustrators, the boy was surprised to see a picture similar to the ones in the "Winnie the Witch" series drawn by Korky Paul (Figure [1]). The style was noticeable in other characters of the same illustrator in different books as well. The capability of a child to easily spot the style was shown to be valid for other illustrators such as Axel Scheffler and Debi Gliori. The boy's enthusiasm let us to start the journey to explore the capabilities of machines to recognize the style of illustrators. We collected pages from children's books to construct a new illustrations dataset consisting of about 6500 pages from 24 artists. We exploited deep networks for categorizing illustrators and with around 94% classification performance our method over-performed the traditional methods by more than 10%. Going beyond categorization we explored transferring style. The classification performance on the transferred images has shown the ability of our system to capture the style. Furthermore, we discovered representative illustrations and discriminative stylistic elements.
Starting Page	338
Ending Page	346
Page Count	9
File Format	PDF
ISBN	9781450347013
DOI	10.1145/3078971.3078982
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Illustrator classification Convolutional neural networks Deep learning for art analysis
Content Type	Text
Resource Type	Article

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