Authentic: Computerized Brushstroke Analysis

Berezhnoy, I.E.; Postma, Eric; Herik, H.J. van den

doi:10.1109/icme.2005.1521739

Cited by 9 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6,7 Increasingly, non-destructive digital techniques are being used to assist art historians in making determinations about the authenticity and attribution of works of art. 5,[8][9][10][11] Nevertheless, many of these techniques are still in their relative infancy, have not been tested on a diverse set of works, and, most importantly, do not address critical outstanding issues in art analysis, such as the way in which the field of conservation can benefit from mathematical tools. As digital archives of museum collections grow and become more accessible to researchers, so too grows the number of potential scientific approaches to examining these works that lie outside of the traditional framework of art historical analysis.…”

mentioning

confidence: 99%

Stylometrics of artwork: uses and limitations

2010

View full text Add to dashboard Cite

A number of digital image analysis techniques have been developed in recent years to address art historical questions. These techniques allow non-destructive analyses of art images that can target outstanding problems of attribution, historical ordering, and other stylistic dimensions. However, great care must be taken in designing the comparisons to which these techniques are applied. In this paper, we review recent work by our lab and by others aimed at establishing a toolbox of stylometrics, and we discuss some of the uses and limitations of these methods. We describe a technique that provides robust classification of authentic drawings by Pieter Bruegel the Elder, and we demonstrate new techniques for art historical analysis applied to the works of other masters. Specifically, we demonstrate the use of two low-level statistics (the slope of the log amplitude spectrum and color histogram correlation) to analyze the works of Picasso and Braque. Finally, we show that face detection and recognition techniques may play a useful role in the attribution of works of art. The rationale for employing vision coding-like methods (e.g., sparse coding) in stylometry is also reviewed. We conclude that generic authentication tools are unlikely to provide reliable stylometric predictions but that with careful construction of comparison sets -which we believe must be done in close collaboration with art historians -these techniques provide important predictions that can be weighed against other art historical evidence. We argue further that concurrent predictions derived from analysis of many independent dimensions of image data (e.g., color, luminance, and spatial statistics) provide the strongest evidence for digital stylometric determinations. MOTIVATIONDespite decades, and in some cases centuries, of analysis, art historians continue to grapple with questions of authenticity and attribution of important works of art. [1][2][3][4][5] Traditionally, methods such as connoisseurship and provenance studies have provided the primary means for attributing works to particular artists. However, since at

show abstract

mentioning

confidence: 99%

Stylometrics of artwork: uses and limitations

2010

View full text Add to dashboard Cite

show abstract

“…Examples include box counting analysis, Fourier spectra, wavelet spectra, brushstroke analysis, etc. (see, e.g., [2], [13], [16], [20], [35], [46]). The different approaches appear to articulate different stylistic elements so that, in short, there is much less agreement on the fundamental analytic elements in stylometry.…”

Section: Introductionmentioning

confidence: 97%

Empirical Mode Decomposition Analysis for Visual Stylometry

Hughes

Mao

Rockmore

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

In this paper, we show how the tools of empirical mode decomposition (EMD) analysis can be applied to the problem of “visual stylometry,” generally defined as the development of quantitative tools for the measurement and comparisons of individual style in the visual arts. In particular, we introduce a new form of EMD analysis for images and show that it is possible to use its output as the basis for the construction of effective support vector machine (SVM)-based stylometric classifiers. We present the methodology and then test it on collections of two sets of digital captures of drawings: a set of authentic and well-known imitations of works attributed to the great Flemish artist Pieter Bruegel the Elder (1525-1569) and a set of works attributed to Dutch master Rembrandt van Rijn (1606-1669) and his pupils. Our positive results indicate that EMD-based methods may hold promise generally as a technique for visual stylometry.

show abstract

“…Different approaches to authentication from different authors include. The different approaches have been tested on a variety of artists, among these Vincent van Gogh, [1][2][3][4][5][6][7][8][9]18 Pieter Bruegel the Elder, [10][11][12][13]21 Jackson Pollock [14][15][16] and Asger Jorn. 21 Many of the approaches rely on the assumption that the brushstrokes in a painting are characteristic for its artist -of which Vincent van Gogh due to his very visible brushstrokes is a very good example.…”

Section: Introductionmentioning

confidence: 99%

“…21 Many of the approaches rely on the assumption that the brushstrokes in a painting are characteristic for its artist -of which Vincent van Gogh due to his very visible brushstrokes is a very good example. 4,6,8,9 As to drawings the way of finishing lines is often said to be characteristic for an artist (personal communication with Niels Borring, National Gallery of Denmark).…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Effects of Data Collection on Visual Stylometry

Jacobsen

Nielsen

2014

Int. J. Image Grap.

View full text Add to dashboard Cite

Visual stylometry is the task of analysing visual art by mathematical and statistical methods. One branch of visual stylometry is to classify if a painting/drawing is made by the claimed artist; that this is achievable has been demonstrated for several artists and by different approaches. The present authors developed a contourlet-based classification method in a previous paper and here it is investigated how robust the conclusions of this method are to variations in the data collection.

show abstract

Authentic: Computerized Brushstroke Analysis

Cited by 9 publications

References 2 publications

Stylometrics of artwork: uses and limitations

Stylometrics of artwork: uses and limitations

Empirical Mode Decomposition Analysis for Visual Stylometry

Investigation of the Effects of Data Collection on Visual Stylometry

Contact Info

Product

Resources

About