Computer analysis of Van Gogh’s complementary colours

Berezhnoy, I.E.; Postma, Eric; Herik, Jaap van den

doi:10.1016/j.patrec.2006.08.002

Cited by 50 publications

(31 citation statements)

References 8 publications

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“…1), their use in paintings and drawings is much more recent. Although this work is still in its relative infancy, statistical methods have shown potential for augmenting traditional approaches to the analysis of visual art by providing new, objective, quantifiable measures that assess artistic style (2)(3)(4)(5), as well as other perceptual dimensions (6)(7)(8)(9). Recent studies have shown that mathematical analyses can produce results in line with accepted art historical findings (2,10).…”

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confidence: 99%

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

Hughes¹,

Graham

Rockmore

2010

Proc. Natl. Acad. Sci. U.S.A.

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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. The statistical approaches that can be applied to the analysis of artistic style are varied, as are the potential applications of these approaches. Wavelet-based techniques are often used [e.g., (2)], as are fractals (3), as well as multiresolution hidden Markov methods (11). In this paper, we bring instead the adaptive technique of sparse coding to bear on the problem. Although originally developed for vision research (12), we show that the principle of sparse coding (finding a set of basis functions that is welladapted for the representation of a given class of images) is useful for accomplishing an image classification task important in the analysis of art. In particular, we show that a sparse coding model is appropriate for distinguishing the styles of different artists. This kind of discriminatory ability could be used to provide statistical evidence for, or against, a particular attribution, a task which is usually known as "authentication."In this paper, we consider the application of sparse coding to a particular authentication task, looking at a problem that has already been attacked by statistical techniques (2, 10): distinguishing a set of secure drawings by the great Flemish artist Pieter Bruegel the Elder (1525-1569) from a set of imitation Bruegels, each of whose attribution is generally accepted among art historians. The drawings in the group of imitations were long thought to be by Bruegel (13), so that their comparison to secure Bruegels is especially interesting. The sparse coding model attempts to create the sparsest possible representation of a given image (or set of images). Thus, a useful statistic for the attribution task is to compare the kurtosis of the representations of the authentic and imitation Bruegels in order to determine their similarity to a control set of authentic Bruegel drawings. Fig. 1 shows the steps involved in our analysis.We find that a sparse coding approach successfully distinguishes the secure...

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confidence: 99%

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

Hughes¹,

Graham

Rockmore

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Diverse feature values including number and orientation standard deviation for brushstrokes for brushstrokes in a neighborhood, size, length, broadness homogeneity and straightness have been calculated statistically, and his unique brushstroke style was provided as scientific evidence. There are also numerous researches on the art works of van Gogh through the analysis of his strongly rhythmic brushstrokes [9,10]. Such analysis on brushstrokes, however, is limited for painters who are very unique like van Gogh.…”

Section: Related Workmentioning

confidence: 99%

Style classification and visualization of art painting’s genre using self-organizing maps

Lee

Cha

2016

Hum. Cent. Comput. Inf. Sci.

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With the spread of digitalization of art paintings, research on diverse scientific approaches on painted images has become active. In this paper, the method of classifying painting styles by extracting various features from paintings is suggested. Global features are extracted using the color-based statistical computation and composition-based local features of paintings are extracted through the segmentation of objects within the paintings to classify the styles of the paintings. Based on the extracted features, paintings are categorized by style using SOM, which are then analyzed through visualization using the map. We have proved the feasibility of the proposed method of categorizing paintings by style, and the objective features of paintings can contribute to the research on art history and aesthetics.

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“…This approach was further extended to the faces of a Perugino painting thought to be painted by several hands; the three faces that clustered in this analysis agreed with the three that art experts think were painted by the same hand. Meanwhile, Berezhnoy and Postma [4,5] found methods for color and texture analysis by complementary colors of VG and specifying the spatial distribution of brushstrokes at different directions by circular filtering. Li and Wang [6] have used texture features obtained by training a 2D Hidden Markov Model on local wavelet coefficients combined with features obtained from detecting and segmenting individual brushstrokes from the images, such as length and average curvature of individual strokes.…”

Section: Previous Workmentioning

confidence: 99%

“…This assumes that an artist's brushwork is characterized by signature features (caused, e.g., by the artist's habitual physical movements) which might be found by machine learning methods and used as an additional piece of evidence to rule upon authenticity. Indeed, early attempts in this area have already found considerable success [2,3,4,5,6]. …”

Section: Introductionmentioning

confidence: 99%

Detection of forgery in paintings using supervised learning

Polatkan

Jafarpour

Brasoveanu

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic [1] has presented some promising initial results. Our reexamination of some of these recently successful experiments shows that variations in image clarity in the experimental datasets were correlated with authenticity, and may have acted as a confounding factor, artificially improving the results. To determine the extent of this factor's influence on previous results, we provide a new "ground truth" data set in which originals and copies are known and image acquisition conditions are uniform. Multiple previously-successful methods are found ineffective on this new confounding-factor-free dataset, but we demonstrate that supervised machine learning on features derived from HiddenMarkov-Tree-modeling of the paintings' wavelet coefficients has the potential to distinguish copies from originals in the new dataset.

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Computer analysis of Van Gogh’s complementary colours

Cited by 50 publications

References 8 publications

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

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

Style classification and visualization of art painting’s genre using self-organizing maps

Detection of forgery in paintings using supervised learning

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