A CNN-LSTM framework for authorship classification of paintings

Kevin Alfianto Jangtjik, Trang Thi Ho, Mei Chen Yeh, Kai Lung Hua

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

4 Citations (Scopus)

Abstract

The authenticity of digital painting image is an urgent demand in the field of art. Yet, determining the authorship of a certain painting is a challenging task due to two reasons: (1) various artists might share similar painting styles; and (2) an artist could create different styles. In this paper, we present a novel method for authorship classification of paintings based on a CNN-LSTM framework. First, a multiscale pyramid is constructed from a painting image. Second, a CNN-LSTM model is learned and it returns possibly multiple labels for one image. To aggregate the final classification result, an adaptive fusion method is employed. Experimental results show that the proposed method has superior classification performance compared with the state-of-the-art techniques.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages2866-2870
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2018 Feb 20
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 2017 Sep 172017 Sep 20

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/9/1717/9/20

Keywords

  • Convolutional neural network
  • Digital image classification
  • Long short-term memory networks
  • Multiscale pyramid representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Jangtjik, K. A., Ho, T. T., Yeh, M. C., & Hua, K. L. (2018). A CNN-LSTM framework for authorship classification of paintings. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 2866-2870). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296806