Mapping the similarity space of paintings: Image statistics and visual perception

Graham, Daniel J.; Friedenberg, Jay; Rockmore, Daniel N.; Field, David

doi:10.1080/13506280902934454

Cited by 42 publications

(37 citation statements)

References 25 publications

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“…Indeed, it has been suggested that the sparseness of the statistical structure of works of art, which can only be detected via higher-order statistics, may contribute to the perception of similarity (6,7,18). The sparse coding model, working with small local patches of images, learns functions that could capture the properties of a particular artist's style, to the extent that these properties are perceptible.…”

Section: Sparse Coding Modelmentioning

confidence: 99%

“…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%

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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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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.

Self Cite

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“…[2,7,8,17,20], however, there is almost no research done on computer-based measuring and determining of influence between artists. Measuring influence is a very difficult task because of the broad criteria for what influence between artists can mean.…”

Section: Introductionmentioning

confidence: 99%

Toward automated discovery of artistic influence

Saleh

Abe

Arora

et al. 2014

Multimed Tools Appl

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Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works

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“…The main focus of the papers is on the artistic identification problem, where the goal is to classify original and fake paintings of a given artist [4,32,39] or to produce stylistic analysis of paintings [20,24,25]. Most of the methods above can be regarded as adaptations from the content-based image retrieval systems [14], where the emphasis is placed on the characterization of brush strokes using texture or color.…”

Section: Literature Reviewmentioning

confidence: 99%

Graph-based methods for the automatic annotation and retrieval of art prints

Carneiro

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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The analysis of images taken from cultural heritage artifacts is an emerging area of research in the field of information retrieval. Current methodologies are focused on the analysis of digital images of paintings for the tasks of forgery detection and style recognition. In this paper, we introduce a graph-based method for the automatic annotation and retrieval of digital images of art prints. Such method can help art historians analyze printed art works using an annotated database of digital images of art prints. The main challenge lies in the fact that art prints generally have limited visual information. The results show that our approach produces better results in a weakly annotated database of art prints in terms of annotation and retrieval performance compared to state-of-the-art approaches based on bag of visual words.H.3.3Information storage and retrievalInformation search and retrieval|Retrieval models I.4.8Image Processing and Computer VisionScene Analysis|Object Recognition G.3Probability and StatisticsProbabilistic Algorithms General TermsAlgorithms, Measurement, Experimentation Keywords Content-based image retrieval and annotation, Art image annotation and retrieval, Graph-based Learning methods

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Mapping the similarity space of paintings: Image statistics and visual perception

Cited by 42 publications

References 25 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

Toward automated discovery of artistic influence

Graph-based methods for the automatic annotation and retrieval of art prints

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