Who Was the Author? An Introduction to Stylometry

Holmes, David; Kardos, Judit

doi:10.1080/09332480.2003.10554842

Cited by 42 publications

(27 citation statements)

References 12 publications

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“…This article is a PNAS Direct Submission. 1 To whom correspondence should be addressed. E-mail: rockmore@cs.dartmouth.edu.…”

Section: Sparse Coding Modelmentioning

confidence: 99%

“…Although such qualitative techniques have a fairly long history in literary analysis (see, e.g., ref. 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).…”

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confidence: 99%

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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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“…This article is a PNAS Direct Submission. 1 To whom correspondence should be addressed. E-mail: rockmore@cs.dartmouth.edu.…”

Section: Sparse Coding Modelmentioning

confidence: 99%

mentioning

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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“…During the last years, more innovative text mining methods have been used for analyses in various fields, e.g., in linguistic stylometry (Girón et al 2005;Nilo and Binongo 2003;Holmes and Kardos 2003), where the probability that a specific author wrote a specific text is calculated by analyzing the author's writing style, or in search engines for learning rankings of documents from search engine logs of user behavior (Radlinski and Joachims 2007).…”

Section: Introductionmentioning

confidence: 99%

Text Mining Infrastructure inR

Feinerer¹,

Hornik²,

Meyer³

2008

J. Stat. Soft.

975

634

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During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

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“…The question arises as to whether a more thorough stylometric analysis would confirm Bately's conclusion that the translation of text 7, The First Fifty Prose Psalms is due to Alfred. Stylometry (Holmes and Kardos 2003) allows for a more extensive analysis, not only of contextual words, but also, and more importantly, of non-contextual words.…”

Section: Introductionmentioning

confidence: 99%

A Stylometric Analysis of King Alfred's Literary Works

Gill¹,

Swartz²,

Treschow³

2007

Journal of Applied Statistics

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For centuries, Alfred the Great was judged to have translated several Latin texts into Old English. Many scholars, however, have expressed doubt whether Alfred could have done all of this work. With the availability of the Old English Corpus in electronic form, it is feasible to subject the texts to statistical stylometric analysis. We approach the problem from a Bayesian perspective where key words are identified and frequencies of the key words are tabulated for seven relevant texts. The question of authorship falls into the general statistical problem of classification where several simple innovations to classical agglomerative procedures are introduced. Our results suggest that one translation that has been traditionally attributed to Alfred (The First Fifty Prose Psalms) tends to distinguish itself from texts that are known to be Alfredian.

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Who Was the Author? An Introduction to Stylometry

Cited by 42 publications

References 12 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

Text Mining Infrastructure inR

A Stylometric Analysis of King Alfred's Literary Works

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