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

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

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

Visual complexity modelling based on image features fusion of multiple kernels

[Abstract] Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans’ perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.Xunta de Galicia; GRC2014/049Portuguese Foundation for Science and Technology; SBIRC; PTDC/EIA EIA/115667/2009Xunta de Galicia; Ref. XUGA-PGIDIT-10TIC105008-PRMinisterio de Ciencia y Tecnología; TIN2008-06562/TINMinisterio de Ecnomía y Competitividad; FJCI-2015-2607

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

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

“I Feel like I Am in That Place and I Would like to See More”: Aesthetic and Embodiment Components of Tourist Destination Image

Photographs of places are cognitive sources that provide the observer with a first, essential impression of a potential tourist destination, before the observer visits that place. Recent evidence suggests that aesthetic qualities of a tourist destination may affect tourists’ experience and satisfaction, contributing to their loyalty toward a destination and intention to return. Drawing upon the literature on sensorimotor processes of aesthetic experience of arts, here, we investigated whether embodiment and aesthetic qualities of landscape photos might play a role in people’s aesthetic preference and willingness to visit a tourist destination. One-hundred twenty-one participants (Mage = 22.17, SD = 6.25) completed an online survey, which asked to evaluate a series of landscapes according to subjective ratings of presence, exploration, and completion, that is the intention to explore beyond the represented place (embodiment dimensions), as well as of symmetry. Furthermore, participants rated how much they liked each destination (Liking) and how much they would like to visit that place (Tourist judgment). Convolutional neural networks (CNN) of image features (Symmetry, Variance and Self-similarity) were also analyzed to rule out the effects of these features on the 2 types of judgment. Results showed that embodiment components predicted both Liking and Tourist judgements. In contrast, neither subjective Symmetry nor CNN measures predicted any of the 2 Liking and Tourist judgements. Overall, our findings support a novel theoretical framework of tourist aesthetic judgment, whereby sensorimotor mechanisms might play a role in tourist destination choice

“I feel like I am in that place and I would like to see more”: Aesthetic and embodiment components of tourist destination image.

Photographs of places are cognitive sources that provide the observer with a first, essential impression of a potential tourist destination, before the observer visits that place. Recent evidence suggests that aesthetic qualities of a tourist destination may affect tourists’ experience and satisfaction, contributing to their loyalty toward a destination and intention to return. Drawing upon the literature on sensorimotor processes of aesthetic experience of arts, here, we investigated whether embodiment and aesthetic qualities of landscape photos might play a role in people’s aesthetic preference and willingness to visit a tourist destination. One-hundred twenty-one participants (Mage = 22.17, SD = 6.25) completed an online survey, which asked to evaluate a series of landscapes according to subjective ratings of presence, exploration, and completion, that is the intention to explore beyond the represented place (embodiment dimensions), as well as of symmetry. Furthermore, participants rated how much they liked each destination (Liking) and how much they would like to visit that place (Tourist judgment). Convolutional neural networks (CNN) of image features (Symmetry, Variance and Self-similarity) were also analyzed to rule out the effects of these features on the 2 types of judgment. Results showed that embodiment components predicted both Liking and Tourist judgements. In contrast, neither subjective Symmetry nor CNN measures predicted any of the 2 Liking and Tourist judgements. Overall, our findings support a novel theoretical framework of tourist aesthetic judgment, whereby sensorimotor mechanisms might play a role in tourist destination choice