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Selective clustering for representative paintings selection

  • Yingying Deng
  • Fan Tang
  • Weiming DongEmail author
  • Fuzhang Wu
  • Oliver Deussen
  • Changsheng Xu
Article
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Abstract

Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods.

Keywords

Digital arts analysis Pattern mining Rejection mechanism Deep feature representation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under nos. 61832016, 61672520 and 61702488, as well as the independent research project of National Laboratory of Pattern Recognition.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.NLPR-LIAMA, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of SoftwareChinese Academy of SciencesBeijingChina
  4. 4.University of KonstanzKonstanzGermany

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