Selective clustering for representative paintings selection View Full Text


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Article Info

DATE

2019-02-09

AUTHORS

Yingying Deng, Fan Tang, Weiming Dong, Fuzhang Wu, Oliver Deussen, Changsheng Xu

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. More... »

PAGES

1-19

References to SciGraph publications

  • 2017-02. Mining Mid-level Visual Patterns with Deep CNN Activations in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015-05. Large-Scale Quantitative Analysis of Painting Arts in SCIENTIFIC REPORTS
  • 1999-06. Fractal analysis of Pollock's drip paintings in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-019-7271-7

    DOI

    http://dx.doi.org/10.1007/s11042-019-7271-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112039128


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