How Self-Similar are Artworks at Different Levels of Spatial Resolution?

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dc.contributor.authorAmirshahi, Seyed Alien_US
dc.contributor.authorRedies, Christophen_US
dc.contributor.authorDenzler, Joachimen_US
dc.contributor.editorDonald House and Cindy Grimmen_US
dc.date.accessioned2016-02-18T10:14:06Z
dc.date.available2016-02-18T10:14:06Z
dc.date.issued2013en_US
dc.description.abstractRecent research has shown that a large variety of aesthetic paintings are highly self-similar. The degree of self-similarity seen in artworks is close to that observed for complex natural scenes, to which low-level visual coding in the human visual system is adapted. In this paper, we introduce a new measure of self-similarity, which we will refer to as the Weighted Self-Similarity (WSS). Using PHOG, which is a state-of-the-art technique from computer vision, WSS is derived from a measure that has been previously linked to aesthetic paintings and represents self-similarity on a single level of spatial resolution. In contrast, WSS takes into account the similarity values at multiple levels of spatial resolution. The values are linked to each other by using a weighting factor so that the overall self-similarity of an image reflects how self-similarity changes at different spatial levels. Compared to the previously proposed metric, WSS has the advantage that it also takes into account differences between selfsimilarity at different levels of spatial resolution with respect to one another. An analysis of a large image dataset of aesthetic artworks (the JenAesthetics dataset) and other categories of images reveals that artworks, on average, show a relatively high WSS. Similarly, high values for WSS were obtained for images of natural patterns that can be described as being fractal (for example, images of clouds, branches or lichen growth patterns). The analysis of the JenAesthetics dataset, which consists of paintings of Western provenance, yielded similar values of WSS for different art styles. In conclusion, self-similarity is uniformly high across different levels of spatial resolution in the artworks analyzed in the present study.en_US
dc.description.sectionheadersImage Processing / Visionen_US
dc.description.seriesinformationComputational Aesthetics in Graphics, Visualization, and Imagingen_US
dc.identifier.doi10.1145/2487276.2487282en_US
dc.identifier.isbn978-1-4503-2203-4en_US
dc.identifier.issn1816-0859en_US
dc.identifier.pages93-100en_US
dc.identifier.urihttps://doi.org/10.1145/2487276.2487282en_US
dc.publisherACMen_US
dc.subjectCR Categoriesen_US
dc.subjectJ.5 [Computer Applications]en_US
dc.subjectArts and Humanitiesen_US
dc.subject[fine arts]en_US
dc.subjectI.5.3 [Pattern Recognition]en_US
dc.subjectClusteringen_US
dc.subject[Similarity measures] G.1.6 [Numerical Analysis]en_US
dc.subjectOptimizationen_US
dc.subject[Gradient methods]en_US
dc.subjectKeywordsen_US
dc.subjectaesthetic quality assessmenten_US
dc.subjectvisual arten_US
dc.subjectpaintingsen_US
dc.subjectWeighted Selfen_US
dc.subjectSimilarityen_US
dc.subjectPyramid of Histograms of Orientationen_US
dc.titleHow Self-Similar are Artworks at Different Levels of Spatial Resolution?en_US
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CompAesth 13: Workshop on Computational Aesthetics