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Adaptive Sparse Coding for Painting Style Analysis

  • Zhi GaoEmail author
  • Mo Shan
  • Loong-Fah Cheong
  • Qingquan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.

Keywords

Sparse Representation Decision Boundary Sparse Code Iterative Reweighted Little Square Paris Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is supported by these Grants: theory and methods of digital conservation for cultural heritage (2012CB725300), PSF Grant 1321202075, and the Singapore NRF under its IRC@SG Funding Initiative and administered by the IDMPO at the SeSaMe centre.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhi Gao
    • 1
    Email author
  • Mo Shan
    • 2
  • Loong-Fah Cheong
    • 3
  • Qingquan Li
    • 4
    • 5
  1. 1.Interactive and Digital Media InstituteNational University of SingaporeSingaporeSingapore
  2. 2.Temasek LaboratoriesNational University of SingaporeSingaporeSingapore
  3. 3.Electrical and Computer Engineering DepartmentNational University of SingaporeSingaporeSingapore
  4. 4.The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhanChina
  5. 5.Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhenChina

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