The Performance Analysis of Low-Resolution Paintings for Computational Aesthetics

  • Juan Zhu
  • Yuan yuan PuEmail author
  • Dan Xu
  • Wen hua Qian
  • Li qing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9317)


In the study of computational aesthetics, we always use high-resolution paintings to analyze painting style, but actually the paintings we obtain mostly are low-resolution. In this paper, the contrast experiments based on sparse coding are carried out between high and low resolution paintings. Different features are extracted in frequency domain and Gabor domain from the basis function of sparse coding (SC). Then the normalized mutual information (NMI) is figured out to analyze the effect of different features for painting style. At last, the features with better performance are used to classify the paintings’ style. The results of experiments show that, to a certain extent, the features extracted from low-resolution paintings still have the ability to characterize the painting style, among which the Gabor energy has the best effect in the painting style analysis.


Computational aesthetics Image resolution Sparse coding Feature extraction Normalized mutual information 



It is a project supported by Natural Science Foundation of P.R. China (No. 61271361, 61263048, 61163019, 61462093), the Research Foundation of Yunnan Province (2014FA021, 2014FB113), and Digital Media Technology Key Laboratory of Universities in Yunnan.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan Zhu
    • 1
  • Yuan yuan Pu
    • 1
    • 2
    Email author
  • Dan Xu
    • 1
    • 2
  • Wen hua Qian
    • 1
  • Li qing Wang
    • 2
  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina
  2. 2.Digital Media Technology Key Laboratory of Universities in YunnanYunnan UniversityKunmingChina

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