Towards Rigorous Study of Artistic Style: A New Psychophysical Paradigm

in Art & Perception
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What makes one artist’s style so different from another’s? How do we perceive these differences? Studying the perception of artistic style has proven difficult. Observers typically view several artworks and must group them or rate similarities between pairs. Responses are often driven by semantic variables, such as scene type or the presence/absence of particular subject matter, which leaves little room for studying how viewers distinguish a Degas ballerina from a Toulouse-Lautrec ballerina, for example. In the current paper, we introduce a new psychophysical paradigm for studying artistic style that focuses on visual qualities and avoids semantic categorization issues by presenting only very local views of a piece, thereby precluding object recognition. The task recasts stylistic judgment in a psychophysical texture discrimination framework, where visual judgments can be rigorously measured for trained and untrained observers alike. Stimuli were a dataset of drawings by Pieter Bruegel the Elder and his imitators studied by the computer science community, which showed that statistical analyses of the drawings’ local content can distinguish an authentic Bruegel from an imitation. Our non-expert observers also successfully discriminated the authentic and inauthentic drawings and furthermore discriminated stylistic variations within the categories, demonstrating the new paradigm’s feasibility for studying artistic style perception. At the same time, however, we discovered several issues in the Bruegel dataset that bear on conclusions drawn by the computer vision studies of artistic style.



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  • Contrasting global and local views. (A) The traditional global view. A large portion of an early Pieter Bruegel the Elder drawing (Table 1 no. 11 courtesy National Gallery of Art, Washington). (B) A psychophysical stimulus of local samples. Focusing on local regions of an image highlights the textural details. We propose a new paradigm where a random selection of image patches is presented to observers as a statistical sampling of the piece’s fine scale character. Patches can be made small enough to preclude recognition of subject matter. The 64 image patches shown here represent less than 4% of (A).

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  • The three-texture discrimination task. The task is to select which of the two side textures belongs to the same category as the reference in the middle. Observers were told that two kinds of images would be used in the experiment and that each texture was made of small image patches sampled from a single larger image. Unbeknownst to the observers, the two categories were authentic and inauthentic Bruegel drawings (Table 1). The patches corresponded to regions approximately 31 × 31 mm in the drawings. Here, the left texture is made of patches from drawing no. 5 (courtesy Leiden University Libraries, Print Room), the right from no. 7 (courtesy Staatliche Graphische Sammlung München), and the reference is no. 11 (courtesy National Gallery of Art, Washington). The correct answer is therefore ‘left’. In our experiments, the drawings were preprocessed to replicate the conditions of previous statistical analyses of style (e.g., via whitening) as well as to rule out trivial image cues (e.g., via normalizing and contrast equalization) and focus viewers on the strokes instead. All image processing operations are explained in Section 4.1.1.

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  • Main results. In Experiment 1, seven naïve, artistically untrained observers discriminated authentic and inauthentic Pieter Bruegel the Elder drawings significantly better than chance (50% correct). Each observer’s percent correct is plotted with the binomial 95% confidence interval, where observers are sorted by overall performance. On average, observers achieved 73% correct (range: 67–83% correct). The most sensitive observer matched the mean level of performance achieved by a sparse coding discriminator applied to the same dataset (Hughes et al., 2010).

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  • Feature identification experiment stimuli. Left textures are again based on patches from drawing no. 5, right textures from no. 7, and the reference is no. 11. The correct answer is ‘left’. (A) Phase scrambled image patches. Stimuli were prepared as in Experiment 1 except that each image patch in a texture was also Fourier phase scrambled. These textures can be directly compared to Fig. 2, which shows the unscrambled version. Phase scrambling preserves some image features but destroys edge coherence and changes the distribution of gray values. (B) Texture model stimuli. Image patches from the drawings were analyzed using the physiologically inspired Portilla–Simoncelli texture model (2000), and new images with the same model features were synthesized. Figure 9 also shows a comparison of original versus synthesized image patches. All of the image processing operations are explained in detail in Section 4.1.1.

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  • Feature identification experiment results. Three observers returned for Experiment 2 (phase scrambling) and 3 (Portilla–Simoncelli textures). Their performance in all three experiments is plotted with 95% confidence intervals. The observers always performed significantly better than chance (50%). All observers performed significantly worse in Experiment 2 (mean = 63% correct) compared to Experiment 1 (mean = 76% correct). Only O6 performed significantly worse in Experiment 3 than in Experiment 1.

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  • Discrimination performance by drawing. We plot the discriminability of each authentic drawing from the class of inauthentic drawings (light gray bars) with 95% confidence intervals, taking into account both hits and false alarms. White bars show false alarm rates for each drawing. Stars indicate where the three observers in Experiments 2 and 3 performed significantly worse than in Experiment 1. (A) Experiment 1. Observers performed significantly above chance with each Bruegel drawing whereas the sparse coding discriminator (dark gray bars) misclassified no. 11 (results from Hughes et al., 2010). (B) Experiment 2. Image patches were phase scrambled. Observers performed significantly better than chance in all cases except for drawing no. 9. Only drawing no. 11 was unaffected by the manipulation. (C) Experiment 3. Image patches were Portilla–Simoncelli texture model samples. Observers discriminated each authentic drawing from the set of inauthentics significantly better than chance. Drawings no. 9, 11, and 13 were unaffected by the manipulation.

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  • Fourier power spectrum whitening. As part of the preprocessing steps used for the sparse coding discriminator, each drawing’s power spectrum was manipulated so that higher spatial frequencies (fine details) were amplified relative to lower spatial frequencies (coarse scale variations). This example shows the effects of whitening on a square section of drawing no. 11 (courtesy National Gallery of Art, Washington). For the purposes of illustration, these images’ contrasts have been amplified slightly. Insets show the Fourier power spectra (power versus spatial frequency) plotted on identical log–log axes. Because the drawings were whitened globally, local image patch Fourier amplitudes varied and were not necessarily white. (A) Before. (B) After.

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  • Histogram equalization of contrast across image patches. For each texture of 100 image patches, we equalized the distribution over the 100 patch r.m.s. contrast values. The two primary benefits of this procedure are (1) it attenuates the appearance of paper texture, and (2) it removes local contrast fluctuations as a potential cue to category identity. Insets show the histograms over patch contrast on identical axes. (To ease comparison, the contrast values in A were normalized to a maximum of 1 before plotting. In B the maximum contrast equals 1 by definition.) This example shows patches from drawing no. 11 (courtesy National Gallery of Art, Washington). (A) Before. (B) After.

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  • Original and synthesized image patches from drawings in the dataset. (A) Patches from a Bruegel drawing (no. 11, courtesy National Gallery of Art, Washington). (B) Patches from an inauthentic drawing (no. 7, courtesy Staatliche Graphische Sammlung München). The upper row shows the original 64 × 64 pixel patches. Directly below we show the synthesized patches with identical Portilla–Simoncelli features. The model represents information in a much lower dimensional space than the raw pixels and captures several textural qualities of the drawings as shown. Each 64 × 64 pixel patch was broken into sixteen 16 × 16 pixel patches for use as stimuli. All details of the image processing operations that were applied are explained in Section 4.1.1.

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