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Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder

机译:通过老彼得·布鲁格绘画中的稀疏编码分析来量化艺术风格

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摘要

Recently, statistical techniques have been used to assist art historians in the analysis of works of art. We present a novel technique for the quantification of artistic style that utilizes a sparse coding model. Originally developed in vision research, sparse coding models can be trained to represent any image space by maximizing the kurtosis of a representation of an arbitrarily selected image from that space. We apply such an analysis to successfully distinguish a set of authentic drawings by Pieter Bruegel the Elder from another set of well-known Bruegel imitations. We show that our approach, which involves a direct comparison based on a single relevant statistic, offers a natural and potentially more germane alternative to wavelet-based classification techniques that rely on more complicated statistical frameworks. Specifically, we show that our model provides a method capable of discriminating between authentic and imitation Bruegel drawings that numerically outperforms well-known existing approaches. Finally, we discuss the applications and constraints of our technique.
机译:最近,统计技术已被用于协助艺术史学家分析艺术品。我们提出了一种利用稀疏编码模型量化艺术风格的新技术。稀疏编码模型最初是在视觉研究中开发的,可通过最大化从该空间任意选择的图像的表示的峰度来训练稀疏编码模型来表示任何图像空间。我们进行这样的分析,以成功地区分老彼得·布鲁格(Pieter Bruegel)和一组著名的布鲁格(Bruegel)模仿。我们表明,我们的方法涉及基于单个相关统计信息的直接比较,它为依赖更复杂统计框架的基于小波的分类技术提供了自然的且可能更紧密的替代方法。具体来说,我们证明了我们的模型提供了一种能够区分真实和模仿Bruegel图纸的方法,该方法在数值上胜过已知的现有方法。最后,我们讨论了技术的应用和约束。

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