Vergleich statistischer Eigenschaften von Bildern aus Werbung, Architektur und Kunst

Braun, Julia GND

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.




Braun, Julia: Vergleich statistischer Eigenschaften von Bildern aus Werbung, Architektur und Kunst. 2016.


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