JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores

Amirshahi, Seyed Ali; Hayn‐Leichsenring, Gregor U.; Denzler, Joachim; Redies, Christoph

doi:10.1007/978-3-319-16178-5_1

Cited by 42 publications

(50 citation statements)

References 33 publications

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“…Self-Similarity, a concept closely related to scale-invariance and fractality, implies that an object has a structure similar to its parts. Museum paintings exhibit a relatively high degree of Self-Similarity compared to other image categories ( Amirshahi et al, 2012 , 2013 ; Redies et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Evaluating Abstract Art: Relation between Term Usage, Subjective Ratings, Image Properties and Personality Traits

2016

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One of the major challenges in experimental aesthetics is the uncertainty of the terminology used in experiments. In this study, we recorded terms that are spontaneously used by participants to describe abstract artworks and studied their relation to the second-order statistical image properties of the same artworks (Experiment 1). We found that the usage frequency of some structure-describing terms correlates with statistical image properties, such as PHOG Self-Similarity, Anisotropy and Complexity. Additionally, emotion-associated terms correlate with measured color values. Next, based on the most frequently used terms, we created five different rating scales (Experiment 2) and obtained ratings of participants for the abstract paintings on these scales. We found significant correlations between descriptive score ratings (e.g., between structure and subjective complexity), between evaluative and descriptive score ratings (e.g., between preference and subjective complexity/interest) and between descriptive score ratings and statistical image properties (e.g., between interest and PHOG Self-Similarity, Complexity and Anisotropy). Additionally, we determined the participants’ personality traits as described in the ‘Big Five Inventory’ (Goldberg, 1990; Rammstedt and John, 2005) and correlated them with the ratings and preferences of individual participants. Participants with higher scores for Neuroticism showed preferences for objectively more complex images, as well as a different notion of the term complex when compared with participants with lower scores for Neuroticism. In conclusion, this study demonstrates an association between objectively measured image properties and the subjective terms that participants use to describe or evaluate abstract artworks. Moreover, our results suggest that the description of abstract artworks, their evaluation and the preference of participants for their low-level statistical properties are linked to personality traits.

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Section: Methodsmentioning

confidence: 99%

Evaluating Abstract Art: Relation between Term Usage, Subjective Ratings, Image Properties and Personality Traits

2016

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“…The following four dataset have been published previously: (i) The Jenaesthetics dataset (Amirshahi et al, 2015 ; Hayn-Leichsenring et al, 2017 ) contains 1,629 high-quality images of oil paintings of Western provenance that were made available by art museums on the Wikimedia Commons webpages (Google Art Project; set no. 1 in Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art

Redies

Brachmann

2017

Front. Neurosci.

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Several statistical image properties have been associated with large subsets of traditional visual artworks. Here, we investigate some of these properties in three categories of art that differ in artistic claim and prestige: (1) Traditional art of different cultural origin from established museums and art collections (oil paintings and graphic art of Western provenance, Islamic book illustration and Chinese paintings), (2) Bad Art from two museums that collect contemporary artworks of lesser importance (© Museum Of Bad Art [MOBA], Somerville, and Official Bad Art Museum of Art [OBAMA], Seattle), and (3) twentieth century abstract art of Western provenance from two prestigious museums (Tate Gallery and Kunstsammlung Nordrhein-Westfalen). We measured the following four statistical image properties: the fractal dimension (a measure relating to subjective complexity); self-similarity (a measure of how much the sections of an image resemble the image as a whole), 1st-order entropy of edge orientations (a measure of how uniformly different orientations are represented in an image); and 2nd-order entropy of edge orientations (a measure of how independent edge orientations are across an image). As shown previously, traditional artworks of different styles share similar values for these measures. The values for Bad Art and twentieth century abstract art show a considerable overlap with those of traditional art, but we also identified numerous examples of Bad Art and abstract art that deviate from traditional art. By measuring statistical image properties, we quantify such differences in image composition for the first time.

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“…In this approach, images are self-similar if the Histograms of Oriented Gradients (HOGs) of parts of an image resemble the HOG of the entire image. Redies et al ( 2012 ) applied this measure to different image categories, ranging from natural scenes to man-made stimuli and artworks, including a large and diverse sets of traditional paintings of Western provenance (Amirshahi et al, 2014b ). For artworks and most natural patterns, Redies and colleagues reported an intermediate to high self-similarity, whereas other patterns, such as images of simple objects, faces of buildings, were less self-similar.…”

Section: Experimental Aesthetics: Investigation Of Specific Image mentioning

confidence: 99%

Computational and Experimental Approaches to Visual Aesthetics

Brachmann

Redies

2017

Front. Comput. Neurosci.

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Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the field of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the field of computational aesthetics. With computing hardware and methodology developing at a high pace, the modeling of perceptually relevant aspect of aesthetic stimuli has a huge potential. In this review, we present an overview of recent developments in computational aesthetics and how they relate to experimental studies. In the first part, we cover topics such as the prediction of ratings, style and artist identification as well as computational methods in art history, such as the detection of influences among artists or forgeries. We also describe currently used computational algorithms, such as classifiers and deep neural networks. In the second part, we summarize results from the field of experimental aesthetics and cover several isolated image properties that are believed to have a effect on the aesthetic appeal of visual stimuli. Their relation to each other and to findings from computational aesthetics are discussed. Moreover, we compare the strategies in the two fields of research and suggest that both fields would greatly profit from a joined research effort. We hope to encourage researchers from both disciplines to work more closely together in order to understand visual aesthetics from an integrated point of view.

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JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores

Cited by 42 publications

References 33 publications

Evaluating Abstract Art: Relation between Term Usage, Subjective Ratings, Image Properties and Personality Traits

Evaluating Abstract Art: Relation between Term Usage, Subjective Ratings, Image Properties and Personality Traits

Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art

Computational and Experimental Approaches to Visual Aesthetics

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