Brushstroke based sparse hybrid convolutional neural networks for author classification of Chinese ink-wash paintings

Meijun Sun, Dong Zhang, Jinchang Ren, Zheng Wang, Jesse S. Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

12 Citations (Scopus)

Abstract

A novel stroke based sparse hybrid convolutional neural networks (CNNs) method is proposed for author classification of Chinese ink-wash paintings (IWPs). As Chinese IWPs usually have many authors in several art styles, this differs from real images or western paintings and has led to a big challenge. In our work, we classify Chinese IWPs of different artists by analyzing a set of automatically extracted brushstrokes. A sparse hybrid CNNs in a deep-learning framework is then proposed to extract brushstroke features to replace the commonly used handcrafted ones such as edge, color, intensity and texture. Using 120 IWPs from six famous artists, promising results have been shown in successfully classifying authors in comparison to two other state-of-the-art approaches.

Original languageEnglish
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE
Pages626-630
Number of pages5
ISBN (Print)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • brushstroke analysis
  • Chinese ink-wash painting
  • convolutional neural networks
  • image classification
  • sparse coding

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