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

Abstract

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...

Visual Link Retrieval in a Database of Paintings

“…As already hinted by [7], using the sum operation instead of max during the pooling phase improves the results. Also, the spatial-pooling proposed by [30] (referred as 2x2-*-pool in the table) allows a very efficient way to incorporate some structure in the image descriptor, improving the R [20] score by 10 %. Although, it is probable this step greatly helps for similar global composition link (i.e.…”

“…As far as analysis of paintings is concerned, most of the previous work actually comes from the Image Processing world with analysis such as brush-stroke extractions and image statistics to perform authorship ( [20] for instance). But the goals and methods are not related to our project.…”

Quantitative criticism of literary relationships

“…Finally, quantitative stylometric analyses have long been used to clarify gross relationships between texts. Standard applications of stylometry include dating literary works and resolving questions of attribution (26)(27)(28)(29)(30). Both ad hoc stylometric analysis and supervised machine learning with stylometric features have proven successful for such applications (31)(32)(33), including for cases in Latin literature (34).…”

Visualizing Natural Image Statistics

“…Despite cultural prominence as canonical "stimuli", scientists only recently began to conduct more rigorous investigations into the statistics of imagery properties of artworks [9]. An important need is to allow quantification of artistic styles, which can lead to new tools for identifying forgeries or determining the source of a work of art [12].…”