Aesthetics of graffiti: Comparison to text-based and pictorial artforms

Graffiti art is a controversial art form, and as such there has been little empirical work assessing its aesthetic value. A recent study examined image statistical properties of text-based artwork and revealed that images of text contain less global structure relative to fine detail compared to artworks. However, previous research did not include graffiti tags or murals, which reside in the space between text and visual art (Melmer et al., 2013). The current study investigated the image statistical properties and attractiveness of graffiti relative to other text-based and pictorial art forms, focusing additionally on the role of expertise. A series of images (N=140; graffiti, text and paintings) were presented to a group of observers with varying degrees of art interest and expertise (N=169). Findings revealed that image statistics predicted attractiveness ratings to images, and that biases against graffiti art are less salient in an expert sample

An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly

Vergleich statistischer Eigenschaften von Bildern aus Werbung, Architektur und Kunst

Most visual advertisements are designed to attract attention, often by inducing a pleasant impression in human observers. Accordingly, results from brain imaging studies show that advertisements can activate the brain’s reward circuitry, which is also involved in the perception of other visually pleasing images, such as aesthetic artworks. At the image level, aesthetic artworks are characterized by specific statistical image properties, such as a high self-similarity (or scale invariance) and intermediate complexity. Moreover, some image properties are distributed uniformly across orientations in the artworks (low anisotropy). In the present study, we asked whether images of advertisements share these properties. To answer this question, large subsets of different types of advertisements (single-product print advertisements, supermarket and department store leaflets, magazine covers and show windows) were analyzed using computer vision algorithms and compared to other types of images (photographs of simple objects, faces, large-vista natural scenes and branches). We show that, on average, images of advertisements have a degree of complexity and self-similarity similar to aesthetic artworks but they are more anisotropic. Values for single-product advertisements resemble each other, independent of the products promoted (cars, cosmetics, fashion or other products). For comparison, we studied images of architecture as another type of visually pleasing stimuli and obtained comparable results. These findings support the general idea that, on average, man-made visually pleasing images are characterized by specific patterns of higher-order (global) image properties that distinguish them from other categories of images. Whether these properties are necessary or sufficient to induce aesthetic perception and how they correlate with brain activation upon viewing advertisements remains to be investigated

Statistical image properties predict aesthetic ratings in abstract paintings created by neural style transfer

Artificial intelligence has emerged as a powerful computational tool to create artworks. One application is Neural Style Transfer, which allows to transfer the style of one image, such as a painting, onto the content of another image, such as a photograph. In the present study, we ask how Neural Style Transfer affects objective image properties and how beholders perceive the novel (style-transferred) stimuli. In order to focus on the subjective perception of artistic style, we minimized the confounding effect of cognitive processing by eliminating all representational content from the input images. To this aim, we transferred the styles of 25 diverse abstract paintings onto 150 colored random-phase patterns with six different Fourier spectral slopes. This procedure resulted in 150 style-transferred stimuli. We then computed eight statistical image properties (complexity, self-similarity, edge-orientation entropy, variances of neural network features, and color statistics) for each image. In a rating study, we asked participants to evaluate the images along three aesthetic dimensions (Pleasing, Harmonious, and Interesting). Results demonstrate that not only objective image properties, but also subjective aesthetic preferences transferred from the original artworks onto the style-transferred images. The image properties of the style-transferred images explain 50 – 69% of the variance in the ratings. In the multidimensional space of statistical image properties, participants considered style-transferred images to be more Pleasing and Interesting if they were closer to a “sweet spot” where traditional Western paintings (JenAesthetics dataset) are represented. We conclude that NST is a useful tool to create novel artistic stimuli that preserve the image properties of the input style images. In the novel stimuli, we found a strong relationship between statistical image properties and subjective ratings, suggesting a prominent role of perceptual processing in the aesthetic evaluation of abstract images

Statistische Bildeigenschaften und Ästhetik

In Rahmen meiner wissenschaftlichen Tätigkeit war ich in den vergangenen Jahren auf dem Gebiet der experimentellen Ästhetik tätig. Die experimentelle Ästhetik ist ein Forschungsgebiet, dessen Ursprung in den Arbeiten Gustav Theodor Fechners gegen Ende des 19. Jahrhunderts liegt. Fechner gilt als der Begründer der so genannten Psychophysik, einer Disziplin, die die Verbindung von messbaren Objekteigenschaften mit subjektiven Empfindungen untersucht (Fechner, 1876). Lag zu Fechners Zeiten allerdings der Fokus der Untersuchung noch auf einfachen mathematischen Konzepten wie dem goldenen Schnitt, werden heutzutage elaboriertere Konzepte wie Raumfrequenzen im Fourier-Spektrum, Komplexität, Anisotropie und Selbstähnlichkeit untersucht. Während der goldene Schnitt ein rein artifiziell-mathematisches Konstrukt ist, versuchen die neueren Konzepte die Bestimmung der objektiven Eigenschaften mit dem Aufbau und der Verarbeitungsweise des menschlichen visuellen Systems in Einklang zu bringen. Es besteht die Grundhypothese, dass durch diese Vorgehensweise begründet werden kann, wieso Bilder mit bestimmten objektiven Eigenschaften von Menschen präferiert werden. Meine Habilitationsschrift ist darauf ausgelegt, die aktuelle Forschungslage bezüglich der Messung von objektiven Bildeigenschaften in Kunstwerken darzulegen und hernach meine eigenen Ergebnisse zu präsentieren, die ich zusammen mit meinen Kollegen in den vergangenen Jahren innerhalb der Juniorarbeitsgruppe „Psychology of Beauty“ am Universitätsklinikum Jena erarbeitet und publiziert habe. Im folgenden Abschnitt sollen zunächst die von mir analysierten Bildeigenschaften genauer dargestellt werden. Danach wird die aktuelle Forschungslage zu globalen Bildeigenschaften in Bezug auf die Attraktivitätsbewertung von Gesichtern und die Schönheitsbewertung von (abstrakten) Kunstwerken dargelegt. Im zweiten Abschnitt werde ich dann die Ergebnisse meiner Arbeitsgruppe zusammenfassend präsentieren

Putting the Art in Artificial: Aesthetic Responses to Computer-generated Art

As artificial intelligence (AI) technology increasingly becomes a feature of everyday life, it is important to understand how creative acts, regarded as uniquely human, can be valued if produced by a machine. The current studies sought to investigate how observers respond to works of visual art created either by humans or by computers. Study 1 tested observers’ ability to discriminate between computer-generated and man-made art, and then examined how categorisation of art works impacted on perceived aesthetic value, revealing a bias against computer-generated art. In Study 2 this bias was reproduced in the context of robotic art, however it was found to be reversed when observers were given the opportunity to see robotic artists in action. These findings reveal an explicit prejudice against computergenerated art, driven largely by the kind of art observers believe computer algorithms are capable of producing. These prejudices can be overridden in circumstances in which observers are able to infer anthropomorphic characteristics in the computer programs, a finding which has implications for the future of artistic AI

Understanding Aesthetics and Fitness Measures in Evolutionary Art Systems

One of the general aims of evolutionary art research is to build a computer systems capable of creating interesting, beautiful or creative results, including images, videos, animations, text, and performances. In this context it is crucial to understand how fitness is conceived and implemented to explore the ‘interestingness’, beauty or creativity that the system is capable of. In this paper we survey the recent research on fitness for evolutionary art related to aesthetics. We also cover research in the psychology of aesthetics, including relation between complexity and aesthetics, measures of complexity and complexity predictors. We try to establish connections between human perception and understanding of aesthetics with current evolutionary techniques

Die Entropie der Verteilung von Kantenorientierungen als Prädiktor für die ästhetische Bewertung verschiedener Bildkategorien

In der vorliegenden Arbeit wird der Einfluss der Entropie der Verteilung von Helligkeitsgradienten als Prädiktor für die ästhetische Bewertung durch Probanden untersucht. Grundlage für die Untersuchungen war die Tatsache, dass in der Ästhetikforschung seit Langem ein Einfluss bestimmter Bildeigenschaften auf die ästhetische Einschätzung von Versuchsteilnehmern bekannt ist. Insbesondere die Bevorzugung runder gegenüber eckiger Objekte ist ein sowohl interindividuell, als auch interkulturell geläufiges Phänomen. Unter den zahlreichen Bildeigenschaften stellte sich die Entropie der Verteilung von Helligkeitsgradienten zunehmend als wichtiger Faktor heraus. Es wird nun näher untersucht, ob die Form der betrachteten Bilder mittels der Entropie der Verteilung ihrer Helligkeitsgradienten umschrieben und diese als Prädiktor für ästhetische Bewertung genutzt werden kann. Die Entropie der Verteilung von Helligkeitsgradienten wird hier mithilfe der Shannon-Methode bestimmt, indem bei einem auf seine Kantenverläufe reduzierten Bild zum einen die Wahrscheinlichkeit des Auftretens bestimmter Kantenorientierungen (Shannon-Entropie 1. Ordnung), zum anderen die Abhängigkeit der einzelnen Kantenorientierungen voneinander (Shannon-Entropie 2. Ordnung) ermittelt wird. Es wurden sowohl bei abstrakten als auch bei natürlichen Bildern statistische Bildeigenschaften erhoben, Bewertungen durch freiwillige Versuchsteilnehmer vorgenommen und diese Daten schließlich mittels einer multiplen Regressionsanalyse miteinander verglichen. Bei einer multiplen Regressionsanalyse wird der Einfluss bestimmter unabhängiger Faktoren, in diesem Falle also der Bildeigenschaften, des Bildinhalts bzw. der Bildkategorie und der Bewertungskategorie auf den sogenannten abhängigen Faktor, in diesem Falle die Bewertung, herausgearbeitet. Es stellte sich heraus, dass die Entropie der Verteilung von Kantenorientierungen bei bestimmten Bildkategorien als Prädiktor für die ästhetische Bewertung fungieren kann. Unter bestimmten Voraussetzungen ist die Entropie der Verteilung von Kantenorientierungen ein besserer Prädiktor als die Form der untersuchten Objekte

Visual aesthetic quotient: Establishing the effects of computational aesthetic measures for servicescape design

Visual aesthetics play a pivotal role in attracting and retaining customers in service environments. Building on theories of environmental psychology, this study introduces a novel and comprehensive aesthetic measure for evaluating servicescape design, which is called as the “visual aesthetic quotient” (VAQ). This measure is presented as the ratio of the dimensions of order and complexity in servicescape’s visual design, and it aims to provide an objective and holistic approach of servicescape design evaluation. In addition, we introduce and validate a pioneering method for quantifying order and complexity objectively using algorithmic models applied to servicescape images. We investigated and established the influence of the VAQ on the perceived attractiveness of servicescapes, developing its role further in this context. The entire approach was comprehensively and rigorously examined using four studies (social media analytics, eye-tracking, a field experiment, and an experimental design), contributing to conceptual advancement and empirical testing. This study provides a novel, computational, objective, and holistic aesthetic measure for effective servicescape design management by validating computational aesthetic measures and establishing their role in influencing servicescape attractiveness; testing the mediation of processing fluency and pleasure; and examining the moderating effects of service context

Edge-Orientation Entropy Predicts Preference for Diverse Types of Man-Made Images

We recently found that luminance edges are more evenly distributed across orientations in large subsets of traditional artworks, i.e., artworks are characterized by a relatively high entropy of edge orientations, when compared to several categories of other (non-art) images. In the present study, we asked whether edge-orientation entropy is associated with aesthetic preference in a wide variety of other man-made visual patterns and scenes. In the first (exploratory) part of the study, participants rated the aesthetic appeal of simple shapes, artificial ornamental patterns, facades of buildings, scenes of interior architecture, and music album covers. Results indicated that edge-orientation entropy predicts aesthetic ratings for these stimuli. However, the magnitude of the effect depended on the type of images analyzed, on the range of entropy values encountered, and on the type of aesthetic rating (pleasing, interesting, or harmonious). For example, edge-orientation entropy predicted about half of the variance when participants rated facade photographs for pleasing and interesting, but only for 3.5% of the variance for harmonious ratings of music album covers. We also asked whether edge-orientation entropy relates to the well-established human preference for curved over angular shapes. Our analysis revealed that edge-orientation entropy was as good or an even better predictor for the aesthetic ratings than curvilinearity. Moreover, entropy could substitute for shape, at least in part, to predict the aesthetic ratings. In the second (experimental) part of this study, we generated complex line stimuli that systematically varied in their edge-orientation entropy and curved/angular shape. Here, edge-orientation entropy was a more powerful predictor for ratings of pleasing and harmonious than curvilinearity, and as good a predictor for interesting. Again, the two image properties shared a large portion of variance between them. In summary, our results indicate that edge-orientation entropy predicts aesthetic ratings in diverse man-made visual stimuli. Moreover, the preference for high edge-orientation entropy shares a large portion of predicted variance with the preference for curved over angular stimuli