Artist-based painting classification using Markov random fields with convolution neural network

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Determining the authorship of a painting image is a challenging task because paintings of an artist may not have a unique style and various artists may have similar painting styles. In this paper, we present a new approach to categorize digital painting images based on artist. We construct a multi-scale pyramid from a painting image to consider both globally and locally the information contained in one image. For each layer, we train a Convolutional Neural Network (CNN) model to determine the class label. To build the relationship within local image patches, we employ Markov random fields (MRFs) by optimizing the Gibbs energy function defined by (1) the data term measuring the compatibility of labeling with given data, and (2) the smoothness term penalizing assignments that label neighboring patches differently. A new fusion scheme is proposed to aggregate patch-level classification results. The proposed CNN-MRF method is validated using two challenging painting image datasets. Experimental results show that the proposed method is effective and achieves state-of-the-art performance.

Original languageEnglish
Pages (from-to)12635-12658
Number of pages24
JournalMultimedia Tools and Applications
Issue number17-18
Publication statusPublished - 2020 May 1


  • Convolutional neural network
  • Image classification
  • Markov random fields
  • Multi-scale pyramid

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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